Removing Outliers based on BET

## [1] "Outliers BET STANDARD DEVIATION: 3qq8dp8jk, 79pn8m6v8, e58u3sinl, urgv6o806"

## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## [1] "Outliers BET SAVED SHEEPS: "
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur

## [1] "Outliers BET EXPLOIT DDA: vuq3c2tk6"
## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## [1] "Total number of outliers:  5"
## [1] "Total number of outliers motor task:  2"
## [1] "Total number of outliers perceptive task:  1"
## [1] "Total number of outliers logical task:  2"

Removing Outliers based on CONFIDENCE SCALE

## [1] "Outliers CS STANDARD DEVIATION: 9b3ph38yc, a6dfu5ljd, dyg7cga2o, tmxmxmwhi, zp9bc59o5"
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur
## [1] "Total number of outliers:  5"
## [1] "Total number of outliers motor task:  0"
## [1] "Total number of outliers perceptive task:  5"
## [1] "Total number of outliers logical task:  0"

Modeling difficulties

Modeling objective difficulty for motor task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   1953.7   1975.3   -972.8   1945.7     1620 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.1396 -0.7500  0.2888  0.7385  2.8481 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.5631   0.7504  
## Number of obs: 1624, groups:  IDjoueur, 56
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -1.0298     0.1873  -5.499 3.83e-08 ***
## difficulty    2.9618     0.2146  13.803  < 2e-16 ***
## timeNorm     -0.5280     0.2020  -2.614  0.00895 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.539       
## timeNorm   -0.571 -0.009
## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.
## 
##  Logique2   Motrice Sensoriel 
##         0      1624         0 
## [1] "Player levels from ranef:"
##   (Intercept)       
##  Min.   :-1.050110  
##  1st Qu.:-0.438217  
##  Median :-0.118832  
##  Mean   :-0.002364  
##  3rd Qu.: 0.296005  
##  Max.   : 1.658440  
## [1] "Intercept: -1.03 3.8e-08 ***"
## [1] "Difficulty: 2.96 2.4e-43 ***"
## [1] "Time: -0.528 0.009 **"
## [1] "R2 fixed: 0.16"
## [1] "R2 mixed: 0.29"
## [1] "Cross Val: 0.68"
## [1] "AIC: 2000"
##         0%        25%        50%        75%       100% 
## -1.6584395 -0.2960052  0.1188317  0.4382172  1.0501105

##         0%        25%        50%        75%       100% 
## -1.6584395 -0.2960052  0.1188317  0.4382172  1.0501105

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

Modeling objective difficulty for sensory task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   1261.1   1282.7   -626.5   1253.1     1620 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -6.3943 -0.3586  0.1131  0.3536  6.6338 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.7241   0.8509  
## Number of obs: 1624, groups:  IDjoueur, 56
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -3.3288     0.2583 -12.885   <2e-16 ***
## difficulty    8.2778     0.4068  20.346   <2e-16 ***
## timeNorm     -0.2933     0.2674  -1.097    0.273    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.650       
## timeNorm   -0.519 -0.046
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control
## $checkConv, : Model failed to converge with max|grad| = 2.21089 (tol =
## 0.001, component 1)
## The result is correct only if all data used by the model has not changed since model was fitted.
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control
## $checkConv, : Model failed to converge with max|grad| = 2.21089 (tol =
## 0.001, component 1)
## The result is correct only if all data used by the model has not changed since model was fitted.
## 
##  Logique2   Motrice Sensoriel 
##         0         0      1624 
## [1] "Player levels from ranef:"
##   (Intercept)        
##  Min.   :-1.6765404  
##  1st Qu.:-0.4435738  
##  Median : 0.0778425  
##  Mean   :-0.0007671  
##  3rd Qu.: 0.4353921  
##  Max.   : 1.5192471  
## [1] "Intercept: -3.33 5.5e-38 ***"
## [1] "Difficulty: 8.28 5e-92 ***"
## [1] "Time: -0.293 0.27 :("
## [1] "R2 fixed: 0.34"
## [1] "R2 mixed: 0.44"
## [1] "Cross Val: 0.82"
## [1] "AIC: 1300"
##          0%         25%         50%         75%        100% 
## -1.51924712 -0.43539206 -0.07784249  0.44357377  1.67654045

##          0%         25%         50%         75%        100% 
## -1.51924712 -0.43539206 -0.07784249  0.44357377  1.67654045

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

Modeling objective difficulty for logical task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   1426.5   1447.8   -709.2   1418.5     1504 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.9435 -0.5021 -0.1156  0.5089  4.9862 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 1.577    1.256   
## Number of obs: 1508, groups:  IDjoueur, 52
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -1.8650     0.2652  -7.033 2.01e-12 ***
## difficulty    5.6686     0.3206  17.680  < 2e-16 ***
## timeNorm     -1.9313     0.2573  -7.507 6.04e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.496       
## timeNorm   -0.378 -0.227
## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.
## 
##  Logique2   Motrice Sensoriel 
##      1508         0         0 
## [1] "Player levels from ranef:"
##   (Intercept)        
##  Min.   :-1.7902825  
##  1st Qu.:-0.7784485  
##  Median :-0.3355504  
##  Mean   :-0.0003123  
##  3rd Qu.: 0.7369882  
##  Max.   : 3.1275697  
## [1] "Intercept: -1.86 2e-12 ***"
## [1] "Difficulty: 5.67 6e-70 ***"
## [1] "Time: -1.93 6e-14 ***"
## [1] "R2 fixed: 0.38"
## [1] "R2 mixed: 0.58"
## [1] "Cross Val: 0.8"
## [1] "AIC: 1400"
##         0%        25%        50%        75%       100% 
## -3.1275697 -0.7369882  0.3355504  0.7784485  1.7902825

##         0%        25%        50%        75%       100% 
## -3.1275697 -0.7369882  0.3355504  0.7784485  1.7902825

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

Influence of Player Profiles

Player profiles

Influence of Player Profiles

Objective level and player profile

Playing video games in general and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.3815, p-value = 0.1671
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1442117

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.68759, p-value = 0.4917
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.07199342

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.30458, p-value = 0.7607
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.03301126

Playing board games in general and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.86453, p-value = 0.3873
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.08913015

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.48979, p-value = 0.6243
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.05061255

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.79975, p-value = 0.4239
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.08596507

Self efficacy and level for each task

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning: Removed 28 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.17852, p-value = 0.8583
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.02429648
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## Warning in cor.test.default(Y, X, method = "kendall"): Removed 28 rows
## containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.4833, p-value = 0.01302
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.3393258 
## 
## [1] "self.eff.on.level.s 0.34 0.013 *"
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning: Removed 26 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.51036, p-value = 0.6098
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.07281435

Risk aversion and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.5679, p-value = 0.1169
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1554335

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.1214, p-value = 0.03389
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.2101231 
## 
## [1] "risk.av.on.level.s 0.21 0.034 *"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.3062, p-value = 0.1915
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1347244

Age and level for each task

## Warning: Removed 1 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.97478, p-value = 0.3297
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.09369113
## Warning: Removed 1 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.2162, p-value = 0.02668
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.2137687 
## 
## [1] "age.on.level.s 0.21 0.027 *"
## Warning: Removed 1 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.2774, p-value = 0.2015
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1275074

Sex and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -2.1404, p-value = 0.03233
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2377395 
## 
## [1] "sexe.on.level.m -0.24 0.032 *"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.077873, p-value = 0.9379
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##          tau 
## -0.008649769

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.26928, p-value = 0.7877
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.03108211

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 220, p-value = 0.03213
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.82775747 -0.05457213
## sample estimates:
## difference in location 
##             -0.4558716 
## 
## [1] "sexe.on.level.m.2 -0.46 0.032 * mean(A): 0.15 mean(B): -0.31"

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 347, p-value = 0.9453
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.4361429  0.4780691
## sample estimates:
## difference in location 
##            -0.01100307

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 292, p-value = 0.7971
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.8271570  0.5994594
## sample estimates:
## difference in location 
##            -0.04046848

Subjective difficulty and play habits

Playing video game in general and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.51384, p-value = 0.6074
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.03114828

Playing board game in general and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -3.5194, p-value = 0.0004325
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2108941 
## 
## [1] "pbg.on.error -0.21 0.00043 ***"

In game level and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.4336, p-value = 0.1517
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.07585348

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.74916, p-value = 0.4538
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.06883117

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.43819, p-value = 0.6613
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.04025974

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.94693, p-value = 0.3437
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.09049774

Sex and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 3.9311, p-value = 8.455e-05
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##      tau 
## 0.253602 
## 
## [1] "sexe.on.error 0.25 8.5e-05 ***"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.9825, p-value = 0.04743
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.2202014 
## 
## [1] "sexe.on.error.m 0.22 0.047 *"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.3795, p-value = 0.01734
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.2642985 
## 
## [1] "sexe.on.error.s 0.26 0.017 *"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.4235, p-value = 0.01537
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##      tau 
## 0.279739 
## 
## [1] "sexe.on.error.l 0.28 0.015 *"

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  B and A
## W = 4126, p-value = 8.517e-05
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  0.05397826 0.12809100
## sample estimates:
## difference in location 
##             0.09264717 
## 
## [1] "sexe.on.error.2 0.093 8.5e-05 *** mean(A): -0.11 mean(B): -0.01"

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 455, p-value = 0.04774
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  0.0005518641 0.1439064370
## sample estimates:
## difference in location 
##             0.07761885 
## 
## [1] "sexe.on.error.m.2 0.078 0.048 * mean(A): -0.097 mean(B): -0.012"

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 489, p-value = 0.01678
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  0.01781275 0.15896562
## sample estimates:
## difference in location 
##             0.09669579 
## 
## [1] "sexe.on.error.s.2 0.097 0.017 * mean(A): -0.11 mean(B): -0.004"

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 432, p-value = 0.01476
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  0.02021314 0.16115025
## sample estimates:
## difference in location 
##              0.1018427 
## 
## [1] "sexe.on.error.l.2 0.1 0.015 * mean(A): -0.12 mean(B): -0.016"

Risk aversion and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.91097, p-value = 0.3623
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.05234983

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.21777, p-value = 0.8276
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.02158799

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.15983, p-value = 0.873
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.01583119

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.2413, p-value = 0.2145
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##     tau 
## 0.12803

Self efficacy and subjective difficulty error

## Warning: Removed 82 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -2.8685, p-value = 0.004124
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2249577 
## 
## [1] "self.eff.on.error -0.22 0.0041 **"
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning: Removed 28 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -1.686, p-value = 0.09179
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2294667 
## 
## [1] "self.eff.on.error -0.23 0.092 ."
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## Warning in cor.test.default(Y, X, method = "kendall"): Removed 28 rows
## containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -1.3708, p-value = 0.1704
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.1873078
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning: Removed 26 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -1.7973, p-value = 0.07228
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2564331 
## 
## [1] "self.eff.on.error -0.26 0.072 ."

Influence of Objective difficulty on Subjective Difficulty

All tasks

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125        0.00054 49     0.84 :(
##  2:      0.09375        0.02500 57     0.21 :(
##  3:      0.15625       -0.01300 57     0.33 :(
##  4:      0.21875        0.02400 57     0.46 :(
##  5:      0.28125       -0.01900 58     0.56 :(
##  6:      0.34375        0.00150 57     0.94 :(
##  7:      0.40625        0.00450 56     0.82 :(
##  8:      0.46875       -0.01600 58     0.65 :(
##  9:      0.53125        0.00450 56     0.84 :(
## 10:      0.59375        0.01100 58     0.78 :(
## 11:      0.65625       -0.05500 58     0.065 .
## 12:      0.71875       -0.10000 58 0.00011 ***
## 13:      0.78125       -0.15000 57 7.8e-08 ***
## 14:      0.84375       -0.18000 55 4.5e-08 ***
## 15:      0.90625       -0.20000 57 4.9e-11 ***
## 16:      0.96875       -0.17000 57 4.9e-11 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 49     0.84 :(
##  2: 57     0.21 :(
##  3: 57     0.33 :(
##  4: 57     0.46 :(
##  5: 58     0.56 :(
##  6: 57     0.94 :(
##  7: 56     0.82 :(
##  8: 58     0.65 :(
##  9: 56     0.84 :(
## 10: 58     0.78 :(
## 11: 58     0.065 .
## 12: 58 0.00011 ***
## 13: 57 7.8e-08 ***
## 14: 55 4.5e-08 ***
## 15: 57 4.9e-11 ***
## 16: 57 4.9e-11 ***
## [1] 56.6
## [1] 2.19

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125        0.00450 34     0.96 :(
##  2:      0.09375        0.00021 36        1 :(
##  3:      0.15625       -0.03100 42     0.32 :(
##  4:      0.21875        0.00800 40     0.89 :(
##  5:      0.28125       -0.01900 38     0.73 :(
##  6:      0.34375        0.03700 38     0.41 :(
##  7:      0.40625        0.02200 40      0.6 :(
##  8:      0.46875        0.01700 38     0.76 :(
##  9:      0.53125        0.04000 37     0.61 :(
## 10:      0.59375        0.00740 40     0.74 :(
## 11:      0.65625       -0.03100 36     0.46 :(
## 12:      0.71875       -0.15000 37 0.00046 ***
## 13:      0.78125       -0.17000 38 0.00037 ***
## 14:      0.84375       -0.20000 25 0.00014 ***
## 15:      0.90625       -0.19000 29 2.4e-06 ***
## 16:      0.96875       -0.15000 19 0.00013 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 34     0.96 :(
##  2: 36        1 :(
##  3: 42     0.32 :(
##  4: 40     0.89 :(
##  5: 38     0.73 :(
##  6: 38     0.41 :(
##  7: 40      0.6 :(
##  8: 38     0.76 :(
##  9: 37     0.61 :(
## 10: 40     0.74 :(
## 11: 36     0.46 :(
## 12: 37 0.00046 ***
## 13: 38 0.00037 ***
## 14: 25 0.00014 ***
## 15: 29 2.4e-06 ***
## 16: 19 0.00013 ***
## [1] 35.4
## [1] 6.11

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125        -0.0310 28     0.23 :(
##  2:      0.09375         0.0490 33     0.098 .
##  3:      0.15625         0.0220 29     0.62 :(
##  4:      0.21875        -0.0045 36     0.63 :(
##  5:      0.28125        -0.0910 33     0.12 :(
##  6:      0.34375        -0.0700 36     0.19 :(
##  7:      0.40625        -0.0320 36      0.4 :(
##  8:      0.46875        -0.0520 34     0.25 :(
##  9:      0.53125         0.0640 35     0.11 :(
## 10:      0.59375         0.0490 33     0.38 :(
## 11:      0.65625        -0.0850 36     0.11 :(
## 12:      0.71875        -0.0760 37     0.088 .
## 13:      0.78125        -0.0670 38      0.01 *
## 14:      0.84375        -0.1400 36 0.00013 ***
## 15:      0.90625        -0.2200 34 3.7e-07 ***
## 16:      0.96875        -0.1800 32 8.2e-07 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 28     0.23 :(
##  2: 33     0.098 .
##  3: 29     0.62 :(
##  4: 36     0.63 :(
##  5: 33     0.12 :(
##  6: 36     0.19 :(
##  7: 36      0.4 :(
##  8: 34     0.25 :(
##  9: 35     0.11 :(
## 10: 33     0.38 :(
## 11: 36     0.11 :(
## 12: 37     0.088 .
## 13: 38      0.01 *
## 14: 36 0.00013 ***
## 15: 34 3.7e-07 ***
## 16: 32 8.2e-07 ***
## [1] 34.1
## [1] 2.75

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125             NA  0          NA
##  2:      0.09375        -0.0220  9     0.72 :(
##  3:      0.15625        -0.0041 12        1 :(
##  4:      0.21875        -0.0045 10     0.47 :(
##  5:      0.28125         0.1500 12     0.19 :(
##  6:      0.34375         0.0850 10     0.54 :(
##  7:      0.40625         0.0940 12     0.22 :(
##  8:      0.46875        -0.0400 15     0.98 :(
##  9:      0.53125        -0.1400 15     0.16 :(
## 10:      0.59375        -0.1300 14     0.19 :(
## 11:      0.65625        -0.0850 14     0.23 :(
## 12:      0.71875        -0.1500 14     0.028 *
## 13:      0.78125        -0.1700 16   0.0048 **
## 14:      0.84375        -0.1700 18     0.012 *
## 15:      0.90625        -0.1500 18 0.00021 ***
## 16:      0.96875        -0.2200 18 0.00021 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1:  9     0.72 :(
##  2: 12        1 :(
##  3: 10     0.47 :(
##  4: 12     0.19 :(
##  5: 10     0.54 :(
##  6: 12     0.22 :(
##  7: 15     0.98 :(
##  8: 15     0.16 :(
##  9: 14     0.19 :(
## 10: 14     0.23 :(
## 11: 14     0.028 *
## 12: 16   0.0048 **
## 13: 18     0.012 *
## 14: 18 0.00021 ***
## 15: 18 0.00021 ***
## [1] 13.8
## [1] 2.96
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

Motor task

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125             NA  0          NA
##  2:      0.09375         -0.094  8     0.21 :(
##  3:      0.15625         -0.099 26     0.015 *
##  4:      0.21875         -0.076 40   0.0065 **
##  5:      0.28125         -0.067 45     0.055 .
##  6:      0.34375         -0.058 47     0.21 :(
##  7:      0.40625         -0.013 49      0.8 :(
##  8:      0.46875          0.031 49     0.73 :(
##  9:      0.53125          0.076 51     0.15 :(
## 10:      0.59375          0.025 51     0.55 :(
## 11:      0.65625         -0.013 53     0.45 :(
## 12:      0.71875         -0.052 51     0.079 .
## 13:      0.78125         -0.067 44     0.029 *
## 14:      0.84375         -0.094 27   0.0073 **
## 15:      0.90625         -0.078 14 0.00076 ***
## 16:      0.96875         -0.110  6     0.034 *
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1:  8     0.21 :(
##  2: 26     0.015 *
##  3: 40   0.0065 **
##  4: 45     0.055 .
##  5: 47     0.21 :(
##  6: 49      0.8 :(
##  7: 49     0.73 :(
##  8: 51     0.15 :(
##  9: 51     0.55 :(
## 10: 53     0.45 :(
## 11: 51     0.079 .
## 12: 44     0.029 *
## 13: 27   0.0073 **
## 14: 14 0.00076 ***
## 15:  6     0.034 *
## [1] 37.4
## [1] 16.7
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n     pval
##  1:      0.03125             NA  0       NA
##  2:      0.09375        -0.0940  8  0.21 :(
##  3:      0.15625        -0.1200 24 0.005 **
##  4:      0.21875        -0.0760 26  0.031 *
##  5:      0.28125        -0.0670 25  0.12 :(
##  6:      0.34375         0.0130 26   0.8 :(
##  7:      0.40625         0.0320 25  0.67 :(
##  8:      0.46875         0.0880 24  0.14 :(
##  9:      0.53125         0.0760 23  0.21 :(
## 10:      0.59375         0.0970 24  0.038 *
## 11:      0.65625         0.0081 25  0.94 :(
## 12:      0.71875        -0.0470 22  0.078 .
## 13:      0.78125        -0.1000 15  0.26 :(
## 14:      0.84375             NA  0       NA
## 15:      0.90625             NA  0       NA
## 16:      0.96875             NA  0       NA
## [1] "mean and sd of nb players per bin"
##     nb     pval
##  1:  8  0.21 :(
##  2: 24 0.005 **
##  3: 26  0.031 *
##  4: 25  0.12 :(
##  5: 26   0.8 :(
##  6: 25  0.67 :(
##  7: 24  0.14 :(
##  8: 23  0.21 :(
##  9: 24  0.038 *
## 10: 25  0.94 :(
## 11: 22  0.078 .
## 12: 15  0.26 :(
## [1] 22.2
## [1] 5.36
## Warning: Removed 4 rows containing missing values (geom_point).
## Warning: Removed 4 rows containing missing values (geom_errorbar).

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125             NA  0        NA
##  2:      0.09375             NA  0        NA
##  3:      0.15625         0.2000  2      1 :(
##  4:      0.21875        -0.2200 14   0.15 :(
##  5:      0.28125        -0.0990 20   0.38 :(
##  6:      0.34375        -0.1600 20    0.08 .
##  7:      0.40625        -0.0490 22   0.31 :(
##  8:      0.46875        -0.0160 21   0.63 :(
##  9:      0.53125         0.1400 21 0.0048 **
## 10:      0.59375         0.0130 21   0.86 :(
## 11:      0.65625        -0.0130 21   0.94 :(
## 12:      0.71875         0.0430 22   0.43 :(
## 13:      0.78125        -0.0099 21   0.75 :(
## 14:      0.84375        -0.0940 19   0.017 *
## 15:      0.90625             NA  6        NA
## 16:      0.96875             NA  0        NA
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1:  2      1 :(
##  2: 14   0.15 :(
##  3: 20   0.38 :(
##  4: 20    0.08 .
##  5: 22   0.31 :(
##  6: 21   0.63 :(
##  7: 21 0.0048 **
##  8: 21   0.86 :(
##  9: 21   0.94 :(
## 10: 22   0.43 :(
## 11: 21   0.75 :(
## 12: 19   0.017 *
## [1] 18.7
## [1] 5.66
## Warning: Removed 4 rows containing missing values (geom_point).
## Warning: Removed 4 rows containing missing values (geom_errorbar).

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj n    pval
##  1:      0.03125             NA 0      NA
##  2:      0.09375             NA 0      NA
##  3:      0.15625             NA 0      NA
##  4:      0.21875             NA 0      NA
##  5:      0.28125             NA 0      NA
##  6:      0.34375             NA 1      NA
##  7:      0.40625         -0.049 2    1 :(
##  8:      0.46875         -0.180 4 0.58 :(
##  9:      0.53125         -0.400 7 0.071 .
## 10:      0.59375         -0.290 6 0.14 :(
## 11:      0.65625         -0.230 7 0.16 :(
## 12:      0.71875         -0.250 7 0.047 *
## 13:      0.78125         -0.180 8 0.023 *
## 14:      0.84375         -0.110 8 0.29 :(
## 15:      0.90625         -0.110 8 0.013 *
## 16:      0.96875         -0.110 6 0.034 *
## [1] "mean and sd of nb players per bin"
##     nb    pval
##  1:  2    1 :(
##  2:  4 0.58 :(
##  3:  7 0.071 .
##  4:  6 0.14 :(
##  5:  7 0.16 :(
##  6:  7 0.047 *
##  7:  8 0.023 *
##  8:  8 0.29 :(
##  9:  8 0.013 *
## 10:  6 0.034 *
## [1] 6.3
## [1] 1.95
## Warning: Removed 6 rows containing missing values (geom_point).
## Warning: Removed 6 rows containing missing values (geom_errorbar).

Sensory task

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         -0.031 44     0.033 *
##  2:      0.09375         -0.094 53     0.014 *
##  3:      0.15625         -0.071 48     0.046 *
##  4:      0.21875         -0.040 40     0.21 :(
##  5:      0.28125         -0.067 38     0.42 :(
##  6:      0.34375         -0.058 36     0.21 :(
##  7:      0.40625         -0.049 37     0.53 :(
##  8:      0.46875         -0.110 37     0.033 *
##  9:      0.53125         -0.140 30     0.027 *
## 10:      0.59375         -0.170 33     0.029 *
## 11:      0.65625         -0.085 34     0.029 *
## 12:      0.71875         -0.150 34   0.0034 **
## 13:      0.78125         -0.210 38 0.00063 ***
## 14:      0.84375         -0.150 45 8.4e-05 ***
## 15:      0.90625         -0.170 53 1.7e-10 ***
## 16:      0.96875         -0.140 56 6.3e-11 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 44     0.033 *
##  2: 53     0.014 *
##  3: 48     0.046 *
##  4: 40     0.21 :(
##  5: 38     0.42 :(
##  6: 36     0.21 :(
##  7: 37     0.53 :(
##  8: 37     0.033 *
##  9: 30     0.027 *
## 10: 33     0.029 *
## 11: 34     0.029 *
## 12: 34   0.0034 **
## 13: 38 0.00063 ***
## 14: 45 8.4e-05 ***
## 15: 53 1.7e-10 ***
## 16: 56 6.3e-11 ***
## [1] 41
## [1] 7.94

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125        -0.0310 19     0.69 :(
##  2:      0.09375        -0.0940 18    0.002 **
##  3:      0.15625        -0.1600 17     0.061 .
##  4:      0.21875        -0.0045 10     0.61 :(
##  5:      0.28125        -0.0670 16     0.51 :(
##  6:      0.34375        -0.2000 12     0.064 .
##  7:      0.40625        -0.1900 12      0.03 *
##  8:      0.46875        -0.2500 15     0.011 *
##  9:      0.53125        -0.3000 11     0.027 *
## 10:      0.59375        -0.2400 12     0.031 *
## 11:      0.65625        -0.1400 12     0.077 .
## 12:      0.71875        -0.3600 11   0.0038 **
## 13:      0.78125        -0.3500 12     0.011 *
## 14:      0.84375        -0.2400 13     0.014 *
## 15:      0.90625        -0.1600 18 0.00019 ***
## 16:      0.96875        -0.1500 19 0.00012 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 19     0.69 :(
##  2: 18    0.002 **
##  3: 17     0.061 .
##  4: 10     0.61 :(
##  5: 16     0.51 :(
##  6: 12     0.064 .
##  7: 12      0.03 *
##  8: 15     0.011 *
##  9: 11     0.027 *
## 10: 12     0.031 *
## 11: 12     0.077 .
## 12: 11   0.0038 **
## 13: 12     0.011 *
## 14: 13     0.014 *
## 15: 18 0.00019 ***
## 16: 19 0.00012 ***
## [1] 14.2
## [1] 3.17

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         -0.031 25   0.0082 **
##  2:      0.09375         -0.094 27     0.31 :(
##  3:      0.15625         -0.120 21     0.058 .
##  4:      0.21875         -0.076 22      0.2 :(
##  5:      0.28125         -0.140 15     0.51 :(
##  6:      0.34375          0.013 19     0.95 :(
##  7:      0.40625          0.022 20     0.72 :(
##  8:      0.46875         -0.110 17     0.25 :(
##  9:      0.53125         -0.100 15     0.44 :(
## 10:      0.59375         -0.150 16     0.31 :(
## 11:      0.65625         -0.160 17     0.17 :(
## 12:      0.71875         -0.076 16     0.15 :(
## 13:      0.78125         -0.067 21     0.11 :(
## 14:      0.84375         -0.130 24   0.0066 **
## 15:      0.90625         -0.190 27 4.7e-06 ***
## 16:      0.96875         -0.140 27 5.5e-06 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 25   0.0082 **
##  2: 27     0.31 :(
##  3: 21     0.058 .
##  4: 22      0.2 :(
##  5: 15     0.51 :(
##  6: 19     0.95 :(
##  7: 20     0.72 :(
##  8: 17     0.25 :(
##  9: 15     0.44 :(
## 10: 16     0.31 :(
## 11: 17     0.17 :(
## 12: 16     0.15 :(
## 13: 21     0.11 :(
## 14: 24   0.0066 **
## 15: 27 4.7e-06 ***
## 16: 27 5.5e-06 ***
## [1] 20.6
## [1] 4.4

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125             NA  0        NA
##  2:      0.09375        -0.0220  8   0.94 :(
##  3:      0.15625         0.0220 10   0.61 :(
##  4:      0.21875         0.0260  8      1 :(
##  5:      0.28125         0.0400  7    0.8 :(
##  6:      0.34375        -0.0220  5   0.78 :(
##  7:      0.40625         0.1200  5   0.44 :(
##  8:      0.46875         0.2100  5   0.19 :(
##  9:      0.53125        -0.0250  4   0.62 :(
## 10:      0.59375        -0.0220  5      1 :(
## 11:      0.65625        -0.0130  5   0.78 :(
## 12:      0.71875         0.0063  7      1 :(
## 13:      0.78125        -0.2100  5   0.19 :(
## 14:      0.84375        -0.0580  8   0.29 :(
## 15:      0.90625        -0.1700  8   0.014 *
## 16:      0.96875        -0.1200 10 0.0059 **
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1:  8   0.94 :(
##  2: 10   0.61 :(
##  3:  8      1 :(
##  4:  7    0.8 :(
##  5:  5   0.78 :(
##  6:  5   0.44 :(
##  7:  5   0.19 :(
##  8:  4   0.62 :(
##  9:  5      1 :(
## 10:  5   0.78 :(
## 11:  7      1 :(
## 12:  5   0.19 :(
## 13:  8   0.29 :(
## 14:  8   0.014 *
## 15: 10 0.0059 **
## [1] 6.67
## [1] 1.95
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

Logical task

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         0.0045 35     0.79 :(
##  2:      0.09375         0.1100 40     0.012 *
##  3:      0.15625         0.1100 40     0.097 .
##  4:      0.21875         0.1600 42   0.0092 **
##  5:      0.28125         0.1500 34     0.051 .
##  6:      0.34375         0.0850 39     0.21 :(
##  7:      0.40625         0.0220 44     0.18 :(
##  8:      0.46875        -0.0045 39     0.93 :(
##  9:      0.53125        -0.0310 37     0.71 :(
## 10:      0.59375        -0.0220 41     0.61 :(
## 11:      0.65625        -0.0490 39     0.42 :(
## 12:      0.71875        -0.1500 38   0.0068 **
## 13:      0.78125        -0.1700 43 0.00035 ***
## 14:      0.84375        -0.2400 41 1.8e-07 ***
## 15:      0.90625        -0.2800 40 3.6e-08 ***
## 16:      0.96875        -0.3300 25 1.3e-05 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 35     0.79 :(
##  2: 40     0.012 *
##  3: 40     0.097 .
##  4: 42   0.0092 **
##  5: 34     0.051 .
##  6: 39     0.21 :(
##  7: 44     0.18 :(
##  8: 39     0.93 :(
##  9: 37     0.71 :(
## 10: 41     0.61 :(
## 11: 39     0.42 :(
## 12: 38   0.0068 **
## 13: 43 0.00035 ***
## 14: 41 1.8e-07 ***
## 15: 40 3.6e-08 ***
## 16: 25 1.3e-05 ***
## [1] 38.6
## [1] 4.47

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         0.0044 26      0.9 :(
##  2:      0.09375         0.0370 26     0.24 :(
##  3:      0.15625         0.0940 24     0.14 :(
##  4:      0.21875         0.1600 24     0.036 *
##  5:      0.28125         0.1100 17     0.32 :(
##  6:      0.34375         0.0850 21     0.24 :(
##  7:      0.40625         0.0940 22     0.25 :(
##  8:      0.46875         0.0670 20     0.44 :(
##  9:      0.53125         0.0400 18     0.46 :(
## 10:      0.59375        -0.0220 21     0.42 :(
## 11:      0.65625        -0.0130 17     0.57 :(
## 12:      0.71875        -0.1500 18     0.097 .
## 13:      0.78125        -0.1400 21     0.026 *
## 14:      0.84375        -0.2000 18 0.00057 ***
## 15:      0.90625        -0.2600 15 0.00071 ***
## 16:      0.96875             NA  1          NA
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 26      0.9 :(
##  2: 26     0.24 :(
##  3: 24     0.14 :(
##  4: 24     0.036 *
##  5: 17     0.32 :(
##  6: 21     0.24 :(
##  7: 22     0.25 :(
##  8: 20     0.44 :(
##  9: 18     0.46 :(
## 10: 21     0.42 :(
## 11: 17     0.57 :(
## 12: 18     0.097 .
## 13: 21     0.026 *
## 14: 18 0.00057 ***
## 15: 15 0.00071 ***
## [1] 20.5
## [1] 3.4
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.064  9      0.9 :(
##  2:      0.09375          0.310 13     0.014 *
##  3:      0.15625          0.240 13     0.16 :(
##  4:      0.21875          0.210 15     0.056 .
##  5:      0.28125          0.076 11     0.31 :(
##  6:      0.34375         -0.033 12        1 :(
##  7:      0.40625         -0.049 15     0.75 :(
##  8:      0.46875         -0.064 12     0.36 :(
##  9:      0.53125         -0.070 11     0.45 :(
## 10:      0.59375          0.085 12     0.22 :(
## 11:      0.65625         -0.160 14     0.19 :(
## 12:      0.71875         -0.290 14     0.032 *
## 13:      0.78125         -0.160 15     0.021 *
## 14:      0.84375         -0.260 15   0.0013 **
## 15:      0.90625         -0.320 15 0.00072 ***
## 16:      0.96875         -0.340 14   0.0011 **
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1:  9      0.9 :(
##  2: 13     0.014 *
##  3: 13     0.16 :(
##  4: 15     0.056 .
##  5: 11     0.31 :(
##  6: 12        1 :(
##  7: 15     0.75 :(
##  8: 12     0.36 :(
##  9: 11     0.45 :(
## 10: 12     0.22 :(
## 11: 14     0.19 :(
## 12: 14     0.032 *
## 13: 15     0.021 *
## 14: 15   0.0013 **
## 15: 15 0.00072 ***
## 16: 14   0.0011 **
## [1] 13.1
## [1] 1.82

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125             NA  0        NA
##  2:      0.09375             NA  1        NA
##  3:      0.15625             NA  3        NA
##  4:      0.21875        -0.0280  3      1 :(
##  5:      0.28125         0.1500  6   0.13 :(
##  6:      0.34375         0.1600  6    0.4 :(
##  7:      0.40625         0.0460  7   0.15 :(
##  8:      0.46875        -0.0016  7      1 :(
##  9:      0.53125        -0.1000  8   0.44 :(
## 10:      0.59375        -0.1700  8   0.36 :(
## 11:      0.65625         0.1300  8   0.29 :(
## 12:      0.71875        -0.1000  6   0.67 :(
## 13:      0.78125        -0.2100  7    0.2 :(
## 14:      0.84375        -0.2700  8   0.042 *
## 15:      0.90625        -0.2600 10 0.0059 **
## 16:      0.96875        -0.3100 10 0.0059 **
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1:  3      1 :(
##  2:  6   0.13 :(
##  3:  6    0.4 :(
##  4:  7   0.15 :(
##  5:  7      1 :(
##  6:  8   0.44 :(
##  7:  8   0.36 :(
##  8:  8   0.29 :(
##  9:  6   0.67 :(
## 10:  7    0.2 :(
## 11:  8   0.042 *
## 12: 10 0.0059 **
## 13: 10 0.0059 **
## [1] 7.23
## [1] 1.83
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_errorbar).

Influence of Playtime on Subjective Difficulty Error

For all groups, motor, sensitive and logical

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.85521  -0.20000   0.03999   0.20805   0.69174  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept) -0.02757    0.02336  -1.180   0.2381   
## timeNorm     0.03482    0.02460   1.416   0.1570   
## obj.diff    -0.08659    0.03066  -2.824   0.0048 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.07429011)
## 
##     Null deviance: 121.29  on 1623  degrees of freedom
## Residual deviance: 120.42  on 1621  degrees of freedom
## AIC: 391.67
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.78610  -0.11567   0.04559   0.11403   0.81494  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.001581   0.016841   0.094    0.925    
## timeNorm     0.009074   0.022514   0.403    0.687    
## obj.diff    -0.212404   0.017421 -12.192   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06402259)
## 
##     Null deviance: 113.31  on 1623  degrees of freedom
## Residual deviance: 103.78  on 1621  degrees of freedom
## AIC: 150.11
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.70209  -0.22699   0.01746   0.22707   0.66363  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.179866   0.023643   7.608 4.89e-14 ***
## timeNorm     0.007075   0.029400   0.241     0.81    
## obj.diff    -0.476741   0.025105 -18.990  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.09502739)
## 
##     Null deviance: 179.81  on 1507  degrees of freedom
## Residual deviance: 143.02  on 1505  degrees of freedom
## AIC: 735.3
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff   n      pval
##  1:      1.5      0.5089286     0.6008109 -0.07683886 112 0.0057 **
##  2:      4.5      0.4889456     0.5714407 -0.07445527 168 8e-04 ***
##  3:      7.5      0.4863946     0.5416953 -0.04763643 168   0.023 *
##  4:     10.5      0.5008503     0.5401276 -0.03488016 168   0.13 :(
##  5:     13.5      0.4447279     0.5174551 -0.06672273 168 0.0017 **
##  6:     16.5      0.4931973     0.5305272 -0.02102698 168   0.36 :(
##  7:     19.5      0.4736395     0.5315528 -0.04770887 168   0.021 *
##  8:     22.5      0.4455782     0.4897264 -0.03529116 168   0.093 .
##  9:     25.5      0.4464286     0.4805683 -0.02474658 168   0.31 :(
## 10:     28.5      0.4166667     0.4572889 -0.03958163 168   0.083 .
##     time  error.diff shapes
##  1:  1.5 -0.07683886     24
##  2:  4.5 -0.07445527     24
##  3:  7.5 -0.04763643     24
##  4: 10.5 -0.03488016     16
##  5: 13.5 -0.06672273     24
##  6: 16.5 -0.02102698     16
##  7: 19.5 -0.04770887     24
##  8: 22.5 -0.03529116     16
##  9: 25.5 -0.02474658     16
## 10: 28.5 -0.03958163     16

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.4285714     0.5941293 -0.14841500 112 7.1e-09 ***
##  2:      4.5      0.5212585     0.6104788 -0.09151470 168 4.2e-08 ***
##  3:      7.5      0.4379252     0.5299114 -0.09240609 168 1.5e-07 ***
##  4:     10.5      0.4642857     0.5824635 -0.10424800 168 1.8e-11 ***
##  5:     13.5      0.4302721     0.5656294 -0.11814246 168 2.1e-13 ***
##  6:     16.5      0.4064626     0.5333505 -0.11438030 168 4.2e-11 ***
##  7:     19.5      0.4685374     0.5641391 -0.08414982 168 1.9e-08 ***
##  8:     22.5      0.4311224     0.5656705 -0.12484954 168 2.4e-12 ***
##  9:     25.5      0.4923469     0.5874740 -0.09752555 168 7.2e-11 ***
## 10:     28.5      0.4608844     0.5711020 -0.10647805 168 1.2e-10 ***
##     time  error.diff shapes
##  1:  1.5 -0.14841500     24
##  2:  4.5 -0.09151470     24
##  3:  7.5 -0.09240609     24
##  4: 10.5 -0.10424800     24
##  5: 13.5 -0.11814246     24
##  6: 16.5 -0.11438030     24
##  7: 19.5 -0.08414982     24
##  8: 22.5 -0.12484954     24
##  9: 25.5 -0.09752555     24
## 10: 28.5 -0.10647805     24

##     time.bin subj.diff.mean obj.diff.mean   error.diff   n        pval
##  1:      1.5      0.4354396     0.5969130 -0.146652665 104 1.6e-06 ***
##  2:      4.5      0.5027473     0.6297636 -0.123587134 156 4.8e-06 ***
##  3:      7.5      0.4972527     0.5544687 -0.063129356 156     0.013 *
##  4:     10.5      0.4908425     0.5229882 -0.045103811 156     0.074 .
##  5:     13.5      0.4734432     0.5312208 -0.047826904 156     0.091 .
##  6:     16.5      0.4661172     0.5008164 -0.043716799 156     0.089 .
##  7:     19.5      0.3937729     0.4456698 -0.053440226 156     0.047 *
##  8:     22.5      0.3864469     0.4198655 -0.032783828 156     0.22 :(
##  9:     25.5      0.3800366     0.3963862 -0.012966789 156     0.66 :(
## 10:     28.5      0.3864469     0.3637653 -0.007979433 156     0.82 :(
##     time   error.diff shapes
##  1:  1.5 -0.146652665     24
##  2:  4.5 -0.123587134     24
##  3:  7.5 -0.063129356     24
##  4: 10.5 -0.045103811     16
##  5: 13.5 -0.047826904     16
##  6: 16.5 -0.043716799     16
##  7: 19.5 -0.053440226     24
##  8: 22.5 -0.032783828     16
##  9: 25.5 -0.012966789     16
## 10: 28.5 -0.007979433     16

For all taks, per group

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTAll[niveau.group == 
##     "bad"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.7386  -0.2157   0.1238   0.1843   0.6876  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.11161    0.04162   2.682  0.00747 ** 
## timeNorm     0.02633    0.03977   0.662  0.50819    
## obj.diff    -0.38311    0.04248  -9.019  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.09817217)
## 
##     Null deviance: 87.748  on 811  degrees of freedom
## Residual deviance: 79.421  on 809  degrees of freedom
## AIC: 424.67
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTAll[niveau.group == 
##     "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.79244  -0.19520   0.05243   0.19503   0.73695  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.06087    0.01960   3.106  0.00192 ** 
## timeNorm     0.03449    0.02347   1.470  0.14185    
## obj.diff    -0.27168    0.02196 -12.373  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.07981968)
## 
##     Null deviance: 163.08  on 1884  degrees of freedom
## Residual deviance: 150.22  on 1882  degrees of freedom
## AIC: 589.14
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTAll[niveau.group == 
##     "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.77200  -0.18126  -0.04443   0.19272   0.77591  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.05465    0.01660   3.292  0.00101 ** 
## timeNorm     0.01940    0.02153   0.901  0.36746    
## obj.diff    -0.25520    0.02078 -12.279  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.07214715)
## 
##     Null deviance: 159.86  on 2058  degrees of freedom
## Residual deviance: 148.33  on 2056  degrees of freedom
## AIC: 434.98
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5      0.5688776     0.7841094 -0.19600695 56 6.9e-07 ***
##  2:      4.5      0.6105442     0.7816709 -0.13781087 84 1.2e-05 ***
##  3:      7.5      0.6105442     0.7784863 -0.13372039 84 6.4e-07 ***
##  4:     10.5      0.5986395     0.7463297 -0.13016904 84 4.9e-05 ***
##  5:     13.5      0.6122449     0.7693833 -0.11874198 84 3.1e-05 ***
##  6:     16.5      0.5561224     0.7336419 -0.13512320 84 1.3e-06 ***
##  7:     19.5      0.5578231     0.7080292 -0.11257040 84    0.001 **
##  8:     22.5      0.5765306     0.7354310 -0.12416835 84   1e-05 ***
##  9:     25.5      0.5238095     0.6935483 -0.13872737 84 1.2e-05 ***
## 10:     28.5      0.5969388     0.6758574 -0.07299896 84   0.0068 **
##     time  error.diff shapes
##  1:  1.5 -0.19600695     24
##  2:  4.5 -0.13781087     24
##  3:  7.5 -0.13372039     24
##  4: 10.5 -0.13016904     24
##  5: 13.5 -0.11874198     24
##  6: 16.5 -0.13512320     24
##  7: 19.5 -0.11257040     24
##  8: 22.5 -0.12416835     24
##  9: 25.5 -0.13872737     24
## 10: 28.5 -0.07299896     24

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.4890110     0.6076861 -0.10832770 130 3.9e-05 ***
##  2:      4.5      0.5355311     0.6661471 -0.11729194 195 1.7e-10 ***
##  3:      7.5      0.4710623     0.5280778 -0.06126488 195   0.0012 **
##  4:     10.5      0.5186813     0.5747535 -0.06479133 195   0.0016 **
##  5:     13.5      0.4820513     0.5737278 -0.08724067 195 2.6e-05 ***
##  6:     16.5      0.4989011     0.5510275 -0.05201895 195     0.015 *
##  7:     19.5      0.4945055     0.5677113 -0.06448681 195 0.00061 ***
##  8:     22.5      0.4395604     0.5156362 -0.08147343 195 0.00021 ***
##  9:     25.5      0.4857143     0.5285704 -0.05412218 195      0.02 *
## 10:     28.5      0.4637363     0.5119649 -0.06102101 195   0.0023 **
##     time  error.diff shapes
##  1:  1.5 -0.10832770     24
##  2:  4.5 -0.11729194     24
##  3:  7.5 -0.06126488     24
##  4: 10.5 -0.06479133     24
##  5: 13.5 -0.08724067     24
##  6: 16.5 -0.05201895     24
##  7: 19.5 -0.06448681     24
##  8: 22.5 -0.08147343     24
##  9: 25.5 -0.05412218     24
## 10: 28.5 -0.06102101     24

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.3863179     0.5141051 -0.11375268 142 4.9e-06 ***
##  2:      4.5      0.4339370     0.4753359 -0.05018826 213     0.019 *
##  3:      7.5      0.4211938     0.4608404 -0.04132774 213     0.039 *
##  4:     10.5      0.4097921     0.4479476 -0.04795599 213     0.013 *
##  5:     13.5      0.3541247     0.4146644 -0.06304349 213 0.00057 ***
##  6:     16.5      0.3749162     0.4121246 -0.04168710 213     0.022 *
##  7:     19.5      0.3588196     0.3916553 -0.04088023 213     0.018 *
##  8:     22.5      0.3447351     0.3778424 -0.03739077 213     0.046 *
##  9:     25.5      0.3675386     0.3752961 -0.01700968 213     0.38 :(
## 10:     28.5      0.3152247     0.3423094 -0.04275993 213     0.027 *
##     time  error.diff shapes
##  1:  1.5 -0.11375268     24
##  2:  4.5 -0.05018826     24
##  3:  7.5 -0.04132774     24
##  4: 10.5 -0.04795599     24
##  5: 13.5 -0.06304349     24
##  6: 16.5 -0.04168710     24
##  7: 19.5 -0.04088023     24
##  8: 22.5 -0.03739077     24
##  9: 25.5 -0.01700968     16
## 10: 28.5 -0.04275993     24

Per group, motor task

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM[niveau.group == 
##     "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.77782  -0.16141   0.07773   0.18154   0.65784  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.462332   0.119085  -3.882 0.000135 ***
## timeNorm     0.004994   0.071725   0.070 0.944555    
## obj.diff     0.309216   0.135862   2.276 0.023773 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.09109535)
## 
##     Null deviance: 21.338  on 231  degrees of freedom
## Residual deviance: 20.861  on 229  degrees of freedom
## AIC: 107.53
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean error.diff  n        pval
##  1:      1.5      0.6339286     0.8544830 -0.1813070 16   0.0013 **
##  2:      4.5      0.5773810     0.7995145 -0.1900501 24 0.00057 ***
##  3:      7.5      0.5714286     0.7551085 -0.1583598 24   0.0043 **
##  4:     10.5      0.5892857     0.7836615 -0.1770491 24   0.0011 **
##  5:     13.5      0.6071429     0.8240112 -0.1620422 24   0.0018 **
##  6:     16.5      0.4821429     0.7818411 -0.2673553 24 0.00028 ***
##  7:     19.5      0.5000000     0.7263256 -0.2097781 24   0.0096 **
##  8:     22.5      0.6130952     0.7654436 -0.1099361 24     0.11 :(
##  9:     25.5      0.5119048     0.7908307 -0.2703569 24 0.00018 ***
## 10:     28.5      0.5476190     0.7394768 -0.1501698 24   0.0087 **
##     time error.diff shapes
##  1:  1.5 -0.1813070     24
##  2:  4.5 -0.1900501     24
##  3:  7.5 -0.1583598     24
##  4: 10.5 -0.1770491     24
##  5: 13.5 -0.1620422     24
##  6: 16.5 -0.2673553     24
##  7: 19.5 -0.2097781     24
##  8: 22.5 -0.1099361     16
##  9: 25.5 -0.2703569     24
## 10: 28.5 -0.1501698     24
## Warning: Removed 2 rows containing missing values (geom_errorbar).

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM[niveau.group == 
##     "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.81780  -0.19138   0.04321   0.17708   0.68822  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept) -0.13079    0.04059  -3.222  0.00134 **
## timeNorm     0.07430    0.03785   1.963  0.05008 . 
## obj.diff     0.09494    0.05455   1.740  0.08228 . 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06868557)
## 
##     Null deviance: 44.015  on 637  degrees of freedom
## Residual deviance: 43.615  on 635  degrees of freedom
## AIC: 106.86
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff  n    pval
##  1:      1.5      0.5227273     0.6251419 -0.087913622 44 0.062 .
##  2:      4.5      0.5432900     0.6224524 -0.067787288 66 0.053 .
##  3:      7.5      0.5064935     0.5482212 -0.033170955 66 0.34 :(
##  4:     10.5      0.5519481     0.5744464 -0.017320378 66  0.7 :(
##  5:     13.5      0.5086580     0.5455378 -0.027725556 66 0.47 :(
##  6:     16.5      0.5519481     0.5560045  0.008925402 66 0.85 :(
##  7:     19.5      0.5519481     0.5704673 -0.010678355 66 0.76 :(
##  8:     22.5      0.4307359     0.5060978 -0.079405018 66 0.035 *
##  9:     25.5      0.4870130     0.4999714 -0.012031106 66 0.76 :(
## 10:     28.5      0.4870130     0.5016324 -0.017813543 66 0.61 :(
##     time   error.diff shapes
##  1:  1.5 -0.087913622     16
##  2:  4.5 -0.067787288     16
##  3:  7.5 -0.033170955     16
##  4: 10.5 -0.017320378     16
##  5: 13.5 -0.027725556     16
##  6: 16.5  0.008925402     16
##  7: 19.5 -0.010678355     16
##  8: 22.5 -0.079405018     24
##  9: 25.5 -0.012031106     16
## 10: 28.5 -0.017813543     16

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM[niveau.group == 
##     "good"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.7823  -0.1833   0.0022   0.2021   0.7030  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -0.08025    0.03159  -2.541   0.0113 *
## timeNorm     0.06361    0.03395   1.874   0.0613 .
## obj.diff     0.06975    0.04864   1.434   0.1520  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06414613)
## 
##     Null deviance: 48.468  on 753  degrees of freedom
## Residual deviance: 48.174  on 751  degrees of freedom
## AIC: 73.823
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff  n    pval
##  1:      1.5      0.4587912     0.5021701 -0.029565591 52  0.5 :(
##  2:      4.5      0.4157509     0.4581003 -0.036201957 78 0.23 :(
##  3:      7.5      0.4432234     0.4705078 -0.025469870 78 0.34 :(
##  4:     10.5      0.4304029     0.4361551 -0.003198821 78 0.91 :(
##  5:     13.5      0.3406593     0.3993679 -0.061812903 78 0.028 *
##  6:     16.5      0.4468864     0.4316421  0.017600071 78 0.45 :(
##  7:     19.5      0.3992674     0.4386951 -0.038308162 78  0.2 :(
##  8:     22.5      0.4065934     0.3910376  0.012148843 78 0.56 :(
##  9:     25.5      0.3919414     0.3686849  0.027863007 78 0.37 :(
## 10:     28.5      0.3168498     0.3329405 -0.021946631 78 0.56 :(
##     time   error.diff shapes
##  1:  1.5 -0.029565591     16
##  2:  4.5 -0.036201957     16
##  3:  7.5 -0.025469870     16
##  4: 10.5 -0.003198821     16
##  5: 13.5 -0.061812903     24
##  6: 16.5  0.017600071     16
##  7: 19.5 -0.038308162     16
##  8: 22.5  0.012148843     16
##  9: 25.5  0.027863007     16
## 10: 28.5 -0.021946631     16

Per group, sensory task

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS[niveau.group == 
##     "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.80757  -0.17473   0.04713   0.10223   0.69034  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.116905   0.047140   2.480   0.0137 *  
## timeNorm    -0.003195   0.056222  -0.057   0.9547    
## obj.diff    -0.297981   0.047296  -6.300 1.11e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.07124426)
## 
##     Null deviance: 23.276  on 289  degrees of freedom
## Residual deviance: 20.447  on 287  degrees of freedom
## AIC: 61.893
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5      0.5428571     0.6390463 -0.11798394 20     0.097 .
##  2:      4.5      0.6666667     0.6686706 -0.02560888 30     0.79 :(
##  3:      7.5      0.5809524     0.7179520 -0.13199277 30   0.0081 **
##  4:     10.5      0.5952381     0.7022945 -0.10466869 30     0.058 .
##  5:     13.5      0.6047619     0.7355270 -0.11701294 30    0.003 **
##  6:     16.5      0.5571429     0.6316433 -0.11730187 30     0.14 :(
##  7:     19.5      0.6000000     0.6735104 -0.09779902 30     0.17 :(
##  8:     22.5      0.6428571     0.7285240 -0.12471305 30     0.012 *
##  9:     25.5      0.4904762     0.6387517 -0.13496951 30 9.2e-06 ***
## 10:     28.5      0.6095238     0.6238117 -0.05083013 30      0.3 :(
##     time  error.diff shapes
##  1:  1.5 -0.11798394     16
##  2:  4.5 -0.02560888     16
##  3:  7.5 -0.13199277     24
##  4: 10.5 -0.10466869     16
##  5: 13.5 -0.11701294     24
##  6: 16.5 -0.11730187     16
##  7: 19.5 -0.09779902     16
##  8: 22.5 -0.12471305     24
##  9: 25.5 -0.13496951     24
## 10: 28.5 -0.05083013     16

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS[niveau.group == 
##     "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.79242  -0.11474   0.04324   0.10863   0.79907  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.01111    0.02414  -0.460    0.646    
## timeNorm     0.04278    0.03211   1.332    0.183    
## obj.diff    -0.20131    0.02507  -8.031 3.56e-15 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06278399)
## 
##     Null deviance: 53.141  on 782  degrees of freedom
## Residual deviance: 48.972  on 780  degrees of freedom
## AIC: 59.665
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5      0.4576720     0.5856813 -0.13077850 54   0.0017 **
##  2:      4.5      0.5255732     0.6540986 -0.10505796 81 3.3e-07 ***
##  3:      7.5      0.4109347     0.4885319 -0.08564427 81   0.0024 **
##  4:     10.5      0.4726631     0.5978029 -0.10758153 81 2.4e-06 ***
##  5:     13.5      0.4550265     0.5802643 -0.11785181 81 1.9e-06 ***
##  6:     16.5      0.4126984     0.5255582 -0.10656642 81 4.5e-05 ***
##  7:     19.5      0.5044092     0.5760814 -0.07137410 81   0.0011 **
##  8:     22.5      0.4179894     0.5370262 -0.11323691 81   4e-05 ***
##  9:     25.5      0.5291005     0.5877937 -0.08344696 81   2e-04 ***
## 10:     28.5      0.4991182     0.5957763 -0.10218991 81 6.6e-07 ***
##     time  error.diff shapes
##  1:  1.5 -0.13077850     24
##  2:  4.5 -0.10505796     24
##  3:  7.5 -0.08564427     24
##  4: 10.5 -0.10758153     24
##  5: 13.5 -0.11785181     24
##  6: 16.5 -0.10656642     24
##  7: 19.5 -0.07137410     24
##  8: 22.5 -0.11323691     24
##  9: 25.5 -0.08344696     24
## 10: 28.5 -0.10218991     24

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS[niveau.group == 
##     "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.68620  -0.11533   0.00271   0.14644   0.85612  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.00914    0.02642  -0.346    0.730    
## timeNorm    -0.03358    0.03694  -0.909    0.364    
## obj.diff    -0.23168    0.02816  -8.228 1.39e-15 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.05848987)
## 
##     Null deviance: 36.064  on 550  degrees of freedom
## Residual deviance: 32.052  on 548  degrees of freedom
## AIC: 4.4272
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5      0.3270677     0.5824937 -0.21975183 38 4.8e-08 ***
##  2:      4.5      0.4385965     0.5178656 -0.07591147 57   0.0016 **
##  3:      7.5      0.4010025     0.4897451 -0.07897515 57 0.00024 ***
##  4:     10.5      0.3834586     0.4975966 -0.09894713 57 1.6e-06 ***
##  5:     13.5      0.3032581     0.4554127 -0.14075030 57 1.1e-06 ***
##  6:     16.5      0.3182957     0.4926908 -0.13608760 57 7.2e-08 ***
##  7:     19.5      0.3483709     0.4896047 -0.10281699 57 2.5e-06 ***
##  8:     22.5      0.3383459     0.5206631 -0.17089406 57 1.1e-07 ***
##  9:     25.5      0.4411028     0.5600315 -0.10217279 57 6.4e-05 ***
## 10:     28.5      0.3283208     0.5082965 -0.14696334 57 4.7e-06 ***
##     time  error.diff shapes
##  1:  1.5 -0.21975183     24
##  2:  4.5 -0.07591147     24
##  3:  7.5 -0.07897515     24
##  4: 10.5 -0.09894713     24
##  5: 13.5 -0.14075030     24
##  6: 16.5 -0.13608760     24
##  7: 19.5 -0.10281699     24
##  8: 22.5 -0.17089406     24
##  9: 25.5 -0.10217279     24
## 10: 28.5 -0.14696334     24

Per group, logical task

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL[niveau.group == 
##     "bad"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.6740  -0.2383   0.1913   0.2315   0.4468  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.31220    0.08931   3.496 0.000547 ***
## timeNorm     0.04250    0.07455   0.570 0.569082    
## obj.diff    -0.66919    0.08659  -7.729 1.84e-13 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1143287)
## 
##     Null deviance: 40.669  on 289  degrees of freedom
## Residual deviance: 32.812  on 287  degrees of freedom
## AIC: 199.05
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5      0.5428571     0.8728737 -0.29669053 20 9.5e-06 ***
##  2:      4.5      0.5809524     0.8803963 -0.25559896 30 5.6e-05 ***
##  3:      7.5      0.6714286     0.8577229 -0.13483882 30 0.00061 ***
##  4:     10.5      0.6095238     0.7604994 -0.13331250 30     0.031 *
##  5:     13.5      0.6238095     0.7595374 -0.13478768 30     0.13 :(
##  6:     16.5      0.6142857     0.7970813 -0.14997730 30   0.0011 **
##  7:     19.5      0.5619048     0.7279108 -0.11259797 30     0.096 .
##  8:     22.5      0.4809524     0.7183280 -0.19119972 30 0.00034 ***
##  9:     25.5      0.5666667     0.6705190 -0.08796937 30     0.35 :(
## 10:     28.5      0.6238095     0.6770076 -0.05763367 30     0.33 :(
##     time  error.diff shapes
##  1:  1.5 -0.29669053     24
##  2:  4.5 -0.25559896     24
##  3:  7.5 -0.13483882     24
##  4: 10.5 -0.13331250     24
##  5: 13.5 -0.13478768     16
##  6: 16.5 -0.14997730     24
##  7: 19.5 -0.11259797     16
##  8: 22.5 -0.19119972     24
##  9: 25.5 -0.08796937     16
## 10: 28.5 -0.05763367     16
## Warning: Removed 2 rows containing missing values (geom_errorbar).

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL[niveau.group == 
##     "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.61833  -0.30332   0.04882   0.26823   0.55284  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.32571    0.04760   6.843 2.48e-11 ***
## timeNorm    -0.07278    0.05505  -1.322    0.187    
## obj.diff    -0.64371    0.05013 -12.842  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1022827)
## 
##     Null deviance: 64.433  on 463  degrees of freedom
## Residual deviance: 47.152  on 461  degrees of freedom
## AIC: 263.84
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff  n        pval
##  1:      1.5      0.4955357     0.6208173 -0.102134051 32     0.054 .
##  2:      4.5      0.5416667     0.7465592 -0.219964008 48 1.4e-05 ***
##  3:      7.5      0.5238095     0.5671145 -0.057721683 48     0.21 :(
##  4:     10.5      0.5505952     0.5362800 -0.012090840 48     0.86 :(
##  5:     13.5      0.4910714     0.6014588 -0.102933948 48     0.058 .
##  6:     16.5      0.5714286     0.5871636 -0.024564724 48      0.7 :(
##  7:     19.5      0.3988095     0.5497972 -0.159240917 48   0.0053 **
##  8:     22.5      0.4880952     0.4926560 -0.007795557 48     0.89 :(
##  9:     25.5      0.4107143     0.4679547 -0.052574583 48     0.41 :(
## 10:     28.5      0.3720238     0.3847404 -0.033401276 48     0.52 :(
##     time   error.diff shapes
##  1:  1.5 -0.102134051     16
##  2:  4.5 -0.219964008     24
##  3:  7.5 -0.057721683     16
##  4: 10.5 -0.012090840     16
##  5: 13.5 -0.102933948     16
##  6: 16.5 -0.024564724     16
##  7: 19.5 -0.159240917     24
##  8: 22.5 -0.007795557     16
##  9: 25.5 -0.052574583     16
## 10: 28.5 -0.033401276     16

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL[niveau.group == 
##     "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.71089  -0.17869  -0.08719   0.21121   0.71297  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.10150    0.02962   3.427 0.000643 ***
## timeNorm     0.04189    0.03875   1.081 0.280065    
## obj.diff    -0.32835    0.03789  -8.665  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.0796471)
## 
##     Null deviance: 67.040  on 753  degrees of freedom
## Residual deviance: 59.815  on 751  degrees of freedom
## AIC: 237.02
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff  n      pval
##  1:      1.5      0.3571429     0.4760639 -0.111779680 52 0.0053 **
##  2:      4.5      0.4487179     0.4614922 -0.022793530 78   0.58 :(
##  3:      7.5      0.4139194     0.4300504 -0.025871902 78   0.57 :(
##  4:     10.5      0.4084249     0.4234581 -0.028539435 78   0.33 :(
##  5:     13.5      0.4047619     0.4001833  0.011787281 78   0.72 :(
##  6:     16.5      0.3443223     0.3337317 -0.003294444 78   0.96 :(
##  7:     19.5      0.3260073     0.2730373  0.026983247 78    0.5 :(
##  8:     22.5      0.2875458     0.2602781  0.022005152 78   0.54 :(
##  9:     25.5      0.2893773     0.2469083  0.020133496 78   0.63 :(
## 10:     28.5      0.3040293     0.2303798  0.036118595 78   0.47 :(
##     time   error.diff shapes
##  1:  1.5 -0.111779680     24
##  2:  4.5 -0.022793530     16
##  3:  7.5 -0.025871902     16
##  4: 10.5 -0.028539435     16
##  5: 13.5  0.011787281     16
##  6: 16.5 -0.003294444     16
##  7: 19.5  0.026983247     16
##  8: 22.5  0.022005152     16
##  9: 25.5  0.020133496     16
## 10: 28.5  0.036118595     16

Influence of Objective difficulty on Subjective Difficulty

All tasks

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.130 49 7.6e-05 ***
##  2:      0.09375          0.160 57 1.3e-05 ***
##  3:      0.15625          0.110 57 0.00037 ***
##  4:      0.21875          0.140 57 2.8e-06 ***
##  5:      0.28125          0.110 58 0.00028 ***
##  6:      0.34375          0.110 57 1.5e-06 ***
##  7:      0.40625          0.069 56     0.013 *
##  8:      0.46875          0.015 58     0.42 :(
##  9:      0.53125         -0.031 56     0.13 :(
## 10:      0.59375         -0.044 58     0.056 .
## 11:      0.65625         -0.110 58 5.4e-05 ***
## 12:      0.71875         -0.130 58 2.4e-06 ***
## 13:      0.78125         -0.190 57 1.8e-08 ***
## 14:      0.84375         -0.230 55 3.7e-09 ***
## 15:      0.90625         -0.240 57 1.2e-10 ***
## 16:      0.96875         -0.190 57 9.9e-10 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 49 7.6e-05 ***
##  2: 57 1.3e-05 ***
##  3: 57 0.00037 ***
##  4: 57 2.8e-06 ***
##  5: 58 0.00028 ***
##  6: 57 1.5e-06 ***
##  7: 56     0.013 *
##  8: 58     0.42 :(
##  9: 56     0.13 :(
## 10: 58     0.056 .
## 11: 58 5.4e-05 ***
## 12: 58 2.4e-06 ***
## 13: 57 1.8e-08 ***
## 14: 55 3.7e-09 ***
## 15: 57 1.2e-10 ***
## 16: 57 9.9e-10 ***
## [1] 56.6
## [1] 2.19

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.074 34   0.0093 **
##  2:      0.09375          0.110 36     0.011 *
##  3:      0.15625          0.094 42     0.011 *
##  4:      0.21875          0.130 40 0.00017 ***
##  5:      0.28125          0.110 38   0.0028 **
##  6:      0.34375          0.120 38 0.00027 ***
##  7:      0.40625          0.077 40     0.022 *
##  8:      0.46875          0.031 38     0.22 :(
##  9:      0.53125         -0.031 37     0.35 :(
## 10:      0.59375         -0.058 40      0.09 .
## 11:      0.65625         -0.098 36     0.027 *
## 12:      0.71875         -0.180 37 1.5e-05 ***
## 13:      0.78125         -0.180 38 6.7e-05 ***
## 14:      0.84375         -0.240 25 0.00013 ***
## 15:      0.90625         -0.260 29   1e-05 ***
## 16:      0.96875         -0.180 19    0.003 **
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 34   0.0093 **
##  2: 36     0.011 *
##  3: 42     0.011 *
##  4: 40 0.00017 ***
##  5: 38   0.0028 **
##  6: 38 0.00027 ***
##  7: 40     0.022 *
##  8: 38     0.22 :(
##  9: 37     0.35 :(
## 10: 40      0.09 .
## 11: 36     0.027 *
## 12: 37 1.5e-05 ***
## 13: 38 6.7e-05 ***
## 14: 25 0.00013 ***
## 15: 29   1e-05 ***
## 16: 19    0.003 **
## [1] 35.4
## [1] 6.11

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.140 28   0.0017 **
##  2:      0.09375          0.160 33 0.00044 ***
##  3:      0.15625          0.120 29   0.0086 **
##  4:      0.21875          0.120 36   0.0031 **
##  5:      0.28125          0.094 33     0.12 :(
##  6:      0.34375          0.110 36     0.017 *
##  7:      0.40625          0.044 36     0.49 :(
##  8:      0.46875         -0.019 34     0.74 :(
##  9:      0.53125         -0.029 35     0.71 :(
## 10:      0.59375         -0.052 33     0.42 :(
## 11:      0.65625         -0.160 36 0.00021 ***
## 12:      0.71875         -0.110 37   0.0026 **
## 13:      0.78125         -0.150 38 7.5e-05 ***
## 14:      0.84375         -0.220 36 6.2e-06 ***
## 15:      0.90625         -0.240 34 1.5e-06 ***
## 16:      0.96875         -0.170 32   8e-06 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 28   0.0017 **
##  2: 33 0.00044 ***
##  3: 29   0.0086 **
##  4: 36   0.0031 **
##  5: 33     0.12 :(
##  6: 36     0.017 *
##  7: 36     0.49 :(
##  8: 34     0.74 :(
##  9: 35     0.71 :(
## 10: 33     0.42 :(
## 11: 36 0.00021 ***
## 12: 37   0.0026 **
## 13: 38 7.5e-05 ***
## 14: 36 6.2e-06 ***
## 15: 34 1.5e-06 ***
## 16: 32   8e-06 ***
## [1] 34.1
## [1] 2.75

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125             NA  0          NA
##  2:      0.09375          0.081  9     0.15 :(
##  3:      0.15625          0.190 12     0.036 *
##  4:      0.21875          0.094 10     0.13 :(
##  5:      0.28125          0.220 12      0.01 *
##  6:      0.34375          0.160 10     0.012 *
##  7:      0.40625          0.160 12     0.065 .
##  8:      0.46875          0.065 15     0.047 *
##  9:      0.53125         -0.031 15      0.04 *
## 10:      0.59375         -0.062 14     0.44 :(
## 11:      0.65625         -0.031 14     0.44 :(
## 12:      0.71875         -0.064 14     0.12 :(
## 13:      0.78125         -0.190 16   0.0065 **
## 14:      0.84375         -0.240 18   0.0012 **
## 15:      0.90625         -0.230 18 0.00055 ***
## 16:      0.96875         -0.310 18 0.00021 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1:  9     0.15 :(
##  2: 12     0.036 *
##  3: 10     0.13 :(
##  4: 12      0.01 *
##  5: 10     0.012 *
##  6: 12     0.065 .
##  7: 15     0.047 *
##  8: 15      0.04 *
##  9: 14     0.44 :(
## 10: 14     0.44 :(
## 11: 14     0.12 :(
## 12: 16   0.0065 **
## 13: 18   0.0012 **
## 14: 18 0.00055 ***
## 15: 18 0.00021 ***
## [1] 13.8
## [1] 2.96
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

Motor task

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125             NA  0          NA
##  2:      0.09375          0.120  8     0.44 :(
##  3:      0.15625          0.094 26     0.44 :(
##  4:      0.21875          0.067 40     0.052 .
##  5:      0.28125          0.069 45      0.09 .
##  6:      0.34375          0.110 47     0.013 *
##  7:      0.40625          0.064 49     0.091 .
##  8:      0.46875          0.048 49     0.045 *
##  9:      0.53125          0.019 51     0.56 :(
## 10:      0.59375         -0.044 51     0.41 :(
## 11:      0.65625         -0.090 53   0.0052 **
## 12:      0.71875         -0.069 51   0.0016 **
## 13:      0.78125         -0.110 44 0.00056 ***
## 14:      0.84375         -0.170 27   0.0029 **
## 15:      0.90625         -0.210 14   0.0094 **
## 16:      0.96875         -0.270  6     0.056 .
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1:  8     0.44 :(
##  2: 26     0.44 :(
##  3: 40     0.052 .
##  4: 45      0.09 .
##  5: 47     0.013 *
##  6: 49     0.091 .
##  7: 49     0.045 *
##  8: 51     0.56 :(
##  9: 51     0.41 :(
## 10: 53   0.0052 **
## 11: 51   0.0016 **
## 12: 44 0.00056 ***
## 13: 27   0.0029 **
## 14: 14   0.0094 **
## 15:  6     0.056 .
## [1] 37.4
## [1] 16.7
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125             NA  0        NA
##  2:      0.09375         0.1200  8   0.44 :(
##  3:      0.15625         0.0770 24   0.52 :(
##  4:      0.21875         0.0680 26   0.13 :(
##  5:      0.28125         0.1100 25   0.029 *
##  6:      0.34375         0.1100 26 0.0021 **
##  7:      0.40625         0.0940 25   0.036 *
##  8:      0.46875         0.1100 24 0.0068 **
##  9:      0.53125         0.0690 23   0.37 :(
## 10:      0.59375         0.0410 24   0.62 :(
## 11:      0.65625        -0.0063 25    0.4 :(
## 12:      0.71875        -0.0690 22   0.054 .
## 13:      0.78125        -0.0810 15   0.042 *
## 14:      0.84375             NA  0        NA
## 15:      0.90625             NA  0        NA
## 16:      0.96875             NA  0        NA
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1:  8   0.44 :(
##  2: 24   0.52 :(
##  3: 26   0.13 :(
##  4: 25   0.029 *
##  5: 26 0.0021 **
##  6: 25   0.036 *
##  7: 24 0.0068 **
##  8: 23   0.37 :(
##  9: 24   0.62 :(
## 10: 25    0.4 :(
## 11: 22   0.054 .
## 12: 15   0.042 *
## [1] 22.2
## [1] 5.36
## Warning: Removed 4 rows containing missing values (geom_point).
## Warning: Removed 4 rows containing missing values (geom_errorbar).

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125             NA  0        NA
##  2:      0.09375             NA  0        NA
##  3:      0.15625          0.290  2      1 :(
##  4:      0.21875          0.073 14   0.23 :(
##  5:      0.28125          0.035 20   0.64 :(
##  6:      0.34375          0.044 20   0.81 :(
##  7:      0.40625          0.014 22   0.95 :(
##  8:      0.46875         -0.019 21   0.68 :(
##  9:      0.53125          0.019 21   0.53 :(
## 10:      0.59375         -0.094 21    0.06 .
## 11:      0.65625         -0.160 21 0.0065 **
## 12:      0.71875         -0.069 22   0.066 .
## 13:      0.78125         -0.081 21    0.07 .
## 14:      0.84375         -0.180 19   0.016 *
## 15:      0.90625         -0.230  6   0.093 .
## 16:      0.96875             NA  0        NA
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1:  2      1 :(
##  2: 14   0.23 :(
##  3: 20   0.64 :(
##  4: 20   0.81 :(
##  5: 22   0.95 :(
##  6: 21   0.68 :(
##  7: 21   0.53 :(
##  8: 21    0.06 .
##  9: 21 0.0065 **
## 10: 22   0.066 .
## 11: 21    0.07 .
## 12: 19   0.016 *
## 13:  6   0.093 .
## [1] 17.7
## [1] 6.46
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_errorbar).

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj n    pval
##  1:      0.03125             NA 0      NA
##  2:      0.09375             NA 0      NA
##  3:      0.15625             NA 0      NA
##  4:      0.21875             NA 0      NA
##  5:      0.28125             NA 0      NA
##  6:      0.34375             NA 1      NA
##  7:      0.40625          0.190 2  0.5 :(
##  8:      0.46875             NA 4      NA
##  9:      0.53125         -0.031 7 0.19 :(
## 10:      0.59375         -0.044 6 0.52 :(
## 11:      0.65625         -0.160 7 0.33 :(
## 12:      0.71875         -0.100 7 0.14 :(
## 13:      0.78125         -0.180 8 0.028 *
## 14:      0.84375         -0.160 8  0.1 :(
## 15:      0.90625         -0.210 8 0.055 .
## 16:      0.96875         -0.270 6 0.056 .
## [1] "mean and sd of nb players per bin"
##    nb    pval
## 1:  2  0.5 :(
## 2:  7 0.19 :(
## 3:  6 0.52 :(
## 4:  7 0.33 :(
## 5:  7 0.14 :(
## 6:  8 0.028 *
## 7:  8  0.1 :(
## 8:  8 0.055 .
## 9:  6 0.056 .
## [1] 6.56
## [1] 1.88
## Warning: Removed 7 rows containing missing values (geom_point).
## Warning: Removed 7 rows containing missing values (geom_errorbar).

Sensory task

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.085 44   0.0094 **
##  2:      0.09375          0.140 53   0.0041 **
##  3:      0.15625          0.094 48     0.081 .
##  4:      0.21875          0.048 40     0.069 .
##  5:      0.28125          0.019 38     0.59 :(
##  6:      0.34375          0.056 36     0.43 :(
##  7:      0.40625         -0.031 37     0.42 :(
##  8:      0.46875         -0.120 37     0.041 *
##  9:      0.53125         -0.210 30 0.00093 ***
## 10:      0.59375         -0.094 33     0.014 *
## 11:      0.65625         -0.160 34 8.1e-05 ***
## 12:      0.71875         -0.220 34 0.00014 ***
## 13:      0.78125         -0.280 38 7.5e-07 ***
## 14:      0.84375         -0.270 45 8.4e-07 ***
## 15:      0.90625         -0.230 53 3.5e-09 ***
## 16:      0.96875         -0.150 56   4e-08 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 44   0.0094 **
##  2: 53   0.0041 **
##  3: 48     0.081 .
##  4: 40     0.069 .
##  5: 38     0.59 :(
##  6: 36     0.43 :(
##  7: 37     0.42 :(
##  8: 37     0.041 *
##  9: 30 0.00093 ***
## 10: 33     0.014 *
## 11: 34 8.1e-05 ***
## 12: 34 0.00014 ***
## 13: 38 7.5e-07 ***
## 14: 45 8.4e-07 ***
## 15: 53 3.5e-09 ***
## 16: 56   4e-08 ***
## [1] 41
## [1] 7.94

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.084 19      0.1 :(
##  2:      0.09375          0.031 18     0.72 :(
##  3:      0.15625          0.094 17     0.31 :(
##  4:      0.21875          0.081 10     0.26 :(
##  5:      0.28125          0.052 16     0.45 :(
##  6:      0.34375         -0.044 12     0.14 :(
##  7:      0.40625         -0.160 12     0.024 *
##  8:      0.46875         -0.170 15     0.018 *
##  9:      0.53125         -0.280 11     0.018 *
## 10:      0.59375         -0.290 12      0.01 *
## 11:      0.65625         -0.280 12   0.0022 **
## 12:      0.71875         -0.340 11   0.0033 **
## 13:      0.78125         -0.280 12   0.0081 **
## 14:      0.84375         -0.340 13   0.0039 **
## 15:      0.90625         -0.230 18 0.00048 ***
## 16:      0.96875         -0.170 19   0.0065 **
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 19      0.1 :(
##  2: 18     0.72 :(
##  3: 17     0.31 :(
##  4: 10     0.26 :(
##  5: 16     0.45 :(
##  6: 12     0.14 :(
##  7: 12     0.024 *
##  8: 15     0.018 *
##  9: 11     0.018 *
## 10: 12      0.01 *
## 11: 12   0.0022 **
## 12: 11   0.0033 **
## 13: 12   0.0081 **
## 14: 13   0.0039 **
## 15: 18 0.00048 ***
## 16: 19   0.0065 **
## [1] 14.2
## [1] 3.17

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         0.0850 25     0.052 .
##  2:      0.09375         0.1600 27     0.012 *
##  3:      0.15625        -0.0062 21     0.97 :(
##  4:      0.21875         0.0310 22     0.24 :(
##  5:      0.28125        -0.0310 15     0.71 :(
##  6:      0.34375         0.0730 19     0.059 .
##  7:      0.40625         0.0380 20     0.87 :(
##  8:      0.46875        -0.0440 17     0.81 :(
##  9:      0.53125        -0.1100 15     0.091 .
## 10:      0.59375        -0.0690 16     0.45 :(
## 11:      0.65625        -0.1600 17     0.017 *
## 12:      0.71875        -0.1200 16      0.03 *
## 13:      0.78125        -0.2300 21 0.00041 ***
## 14:      0.84375        -0.2400 24 0.00092 ***
## 15:      0.90625        -0.2100 27   5e-05 ***
## 16:      0.96875        -0.0800 27 0.00018 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 25     0.052 .
##  2: 27     0.012 *
##  3: 21     0.97 :(
##  4: 22     0.24 :(
##  5: 15     0.71 :(
##  6: 19     0.059 .
##  7: 20     0.87 :(
##  8: 17     0.81 :(
##  9: 15     0.091 .
## 10: 16     0.45 :(
## 11: 17     0.017 *
## 12: 16      0.03 *
## 13: 21 0.00041 ***
## 14: 24 0.00092 ***
## 15: 27   5e-05 ***
## 16: 27 0.00018 ***
## [1] 20.6
## [1] 4.4

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125             NA  0        NA
##  2:      0.09375          0.140  8    0.1 :(
##  3:      0.15625          0.190 10    0.04 *
##  4:      0.21875          0.070  8   0.44 :(
##  5:      0.28125          0.094  7   0.44 :(
##  6:      0.34375          0.081  5   0.18 :(
##  7:      0.40625          0.110  5   0.44 :(
##  8:      0.46875         -0.120  5   0.78 :(
##  9:      0.53125         -0.260  4   0.12 :(
## 10:      0.59375         -0.094  5   0.58 :(
## 11:      0.65625         -0.160  5   0.41 :(
## 12:      0.71875         -0.074  7   0.55 :(
## 13:      0.78125         -0.280  5   0.054 .
## 14:      0.84375         -0.220  8   0.041 *
## 15:      0.90625         -0.290  8   0.014 *
## 16:      0.96875         -0.240 10 0.0059 **
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1:  8    0.1 :(
##  2: 10    0.04 *
##  3:  8   0.44 :(
##  4:  7   0.44 :(
##  5:  5   0.18 :(
##  6:  5   0.44 :(
##  7:  5   0.78 :(
##  8:  4   0.12 :(
##  9:  5   0.58 :(
## 10:  5   0.41 :(
## 11:  7   0.55 :(
## 12:  5   0.054 .
## 13:  8   0.041 *
## 14:  8   0.014 *
## 15: 10 0.0059 **
## [1] 6.67
## [1] 1.95
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

Logical task

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.089 35     0.017 *
##  2:      0.09375          0.160 40 5.2e-05 ***
##  3:      0.15625          0.150 40 0.00025 ***
##  4:      0.21875          0.230 42 9.5e-06 ***
##  5:      0.28125          0.220 34 0.00028 ***
##  6:      0.34375          0.160 39 5.5e-05 ***
##  7:      0.40625          0.094 44     0.011 *
##  8:      0.46875          0.031 39     0.024 *
##  9:      0.53125         -0.031 37     0.21 :(
## 10:      0.59375         -0.019 41     0.77 :(
## 11:      0.65625         -0.018 39     0.68 :(
## 12:      0.71875         -0.100 38    0.002 **
## 13:      0.78125         -0.160 43 9.5e-05 ***
## 14:      0.84375         -0.220 41 6.5e-07 ***
## 15:      0.90625         -0.260 40 3.4e-07 ***
## 16:      0.96875         -0.340 25 1.4e-05 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 35     0.017 *
##  2: 40 5.2e-05 ***
##  3: 40 0.00025 ***
##  4: 42 9.5e-06 ***
##  5: 34 0.00028 ***
##  6: 39 5.5e-05 ***
##  7: 44     0.011 *
##  8: 39     0.024 *
##  9: 37     0.21 :(
## 10: 41     0.77 :(
## 11: 39     0.68 :(
## 12: 38    0.002 **
## 13: 43 9.5e-05 ***
## 14: 41 6.5e-07 ***
## 15: 40 3.4e-07 ***
## 16: 25 1.4e-05 ***
## [1] 38.6
## [1] 4.47

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125          0.050 26   0.071 .
##  2:      0.09375          0.110 26  0.007 **
##  3:      0.15625          0.110 24   0.027 *
##  4:      0.21875          0.200 24 0.0014 **
##  5:      0.28125          0.140 17   0.13 :(
##  6:      0.34375          0.160 21   0.036 *
##  7:      0.40625          0.120 22   0.085 .
##  8:      0.46875          0.031 20   0.15 :(
##  9:      0.53125         -0.031 18   0.27 :(
## 10:      0.59375         -0.094 21   0.19 :(
## 11:      0.65625         -0.056 17   0.57 :(
## 12:      0.71875         -0.120 18   0.026 *
## 13:      0.78125         -0.160 21 0.0081 **
## 14:      0.84375         -0.220 18 0.0018 **
## 15:      0.90625         -0.310 15 0.0024 **
## 16:      0.96875             NA  1        NA
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1: 26   0.071 .
##  2: 26  0.007 **
##  3: 24   0.027 *
##  4: 24 0.0014 **
##  5: 17   0.13 :(
##  6: 21   0.036 *
##  7: 22   0.085 .
##  8: 20   0.15 :(
##  9: 18   0.27 :(
## 10: 21   0.19 :(
## 11: 17   0.57 :(
## 12: 18   0.026 *
## 13: 21 0.0081 **
## 14: 18 0.0018 **
## 15: 15 0.0024 **
## [1] 20.5
## [1] 3.4
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125          0.140  9   0.15 :(
##  2:      0.09375          0.310 13 0.0032 **
##  3:      0.15625          0.340 13 0.0026 **
##  4:      0.21875          0.280 15 0.0047 **
##  5:      0.28125          0.220 11 0.0037 **
##  6:      0.34375          0.180 12 0.0026 **
##  7:      0.40625          0.094 15   0.37 :(
##  8:      0.46875          0.031 12   0.21 :(
##  9:      0.53125         -0.031 11   0.61 :(
## 10:      0.59375          0.059 12   0.11 :(
## 11:      0.65625         -0.056 14   0.25 :(
## 12:      0.71875         -0.220 14   0.036 *
## 13:      0.78125         -0.210 15 0.0039 **
## 14:      0.84375         -0.220 15 0.0011 **
## 15:      0.90625         -0.250 15 0.0013 **
## 16:      0.96875         -0.330 14 0.0013 **
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1:  9   0.15 :(
##  2: 13 0.0032 **
##  3: 13 0.0026 **
##  4: 15 0.0047 **
##  5: 11 0.0037 **
##  6: 12 0.0026 **
##  7: 15   0.37 :(
##  8: 12   0.21 :(
##  9: 11   0.61 :(
## 10: 12   0.11 :(
## 11: 14   0.25 :(
## 12: 14   0.036 *
## 13: 15 0.0039 **
## 14: 15 0.0011 **
## 15: 15 0.0013 **
## 16: 14 0.0013 **
## [1] 13.1
## [1] 1.82

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125             NA  0        NA
##  2:      0.09375             NA  1        NA
##  3:      0.15625             NA  3        NA
##  4:      0.21875          0.210  3   0.25 :(
##  5:      0.28125          0.420  6   0.058 .
##  6:      0.34375          0.210  6   0.056 .
##  7:      0.40625          0.290  7   0.11 :(
##  8:      0.46875          0.190  7    0.2 :(
##  9:      0.53125         -0.031  8   0.72 :(
## 10:      0.59375         -0.041  8   0.72 :(
## 11:      0.65625          0.094  8   0.29 :(
## 12:      0.71875         -0.069  6   0.53 :(
## 13:      0.78125         -0.068  7   0.67 :(
## 14:      0.84375         -0.220  8   0.041 *
## 15:      0.90625         -0.260 10   0.014 *
## 16:      0.96875         -0.370 10 0.0059 **
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1:  3   0.25 :(
##  2:  6   0.058 .
##  3:  6   0.056 .
##  4:  7   0.11 :(
##  5:  7    0.2 :(
##  6:  8   0.72 :(
##  7:  8   0.72 :(
##  8:  8   0.29 :(
##  9:  6   0.53 :(
## 10:  7   0.67 :(
## 11:  8   0.041 *
## 12: 10   0.014 *
## 13: 10 0.0059 **
## [1] 7.23
## [1] 1.83
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_errorbar).

Influence of Playtime on Subjective Difficulty Error

For all groups, motor, sensitive and logical

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTM)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.70319  -0.16766   0.00799   0.17682   0.64502  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.17379    0.01995   8.711   <2e-16 ***
## timeNorm     0.00431    0.02101   0.205    0.837    
## obj.diff    -0.37273    0.02619 -14.234   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.05419176)
## 
##     Null deviance: 99.193  on 1623  degrees of freedom
## Residual deviance: 87.845  on 1621  degrees of freedom
## AIC: -120.62
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTS)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.77933  -0.20089  -0.03724   0.24111   0.77727  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.10795    0.01735   6.220  6.3e-10 ***
## timeNorm     0.03878    0.02320   1.672   0.0948 .  
## obj.diff    -0.36404    0.01795 -20.278  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06798547)
## 
##     Null deviance: 138.38  on 1623  degrees of freedom
## Residual deviance: 110.20  on 1621  degrees of freedom
## AIC: 247.65
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTL)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.74305  -0.21400  -0.02148   0.20096   0.71922  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.20615    0.02036  10.127  < 2e-16 ***
## timeNorm     0.06739    0.02531   2.662  0.00785 ** 
## obj.diff    -0.51720    0.02162 -23.927  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.07044787)
## 
##     Null deviance: 151.98  on 1507  degrees of freedom
## Residual deviance: 106.02  on 1505  degrees of freedom
## AIC: 283.97
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean    error.diff   n    pval
##  1:      1.5      0.5348214     0.6008109 -0.0627956484 112 0.021 *
##  2:      4.5      0.5291667     0.5714407 -0.0363491910 168 0.071 .
##  3:      7.5      0.5071429     0.5416953 -0.0317522384 168 0.12 :(
##  4:     10.5      0.5339286     0.5401276  0.0027661467 168 0.89 :(
##  5:     13.5      0.5071429     0.5174551 -0.0066784780 168 0.74 :(
##  6:     16.5      0.5232143     0.5305272 -0.0054376495 168 0.78 :(
##  7:     19.5      0.4976190     0.5315528 -0.0349803686 168 0.062 .
##  8:     22.5      0.4779762     0.4897264 -0.0103383643 168 0.64 :(
##  9:     25.5      0.4797619     0.4805683  0.0009212402 168 0.95 :(
## 10:     28.5      0.4642857     0.4572889  0.0071690193 168 0.72 :(
##     time    error.diff shapes
##  1:  1.5 -0.0627956484     24
##  2:  4.5 -0.0363491910     16
##  3:  7.5 -0.0317522384     16
##  4: 10.5  0.0027661467     16
##  5: 13.5 -0.0066784780     16
##  6: 16.5 -0.0054376495     16
##  7: 19.5 -0.0349803686     16
##  8: 22.5 -0.0103383643     16
##  9: 25.5  0.0009212402     16
## 10: 28.5  0.0071690193     16

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.4696429     0.5941293 -0.13598057 112 2.7e-05 ***
##  2:      4.5      0.5125000     0.6104788 -0.08536726 168 5.7e-06 ***
##  3:      7.5      0.4666667     0.5299114 -0.06382281 168   0.0032 **
##  4:     10.5      0.5148810     0.5824635 -0.06568890 168   0.0015 **
##  5:     13.5      0.4773810     0.5656294 -0.08101223 168 1.6e-05 ***
##  6:     16.5      0.4345238     0.5333505 -0.10807690 168 6.4e-06 ***
##  7:     19.5      0.4875000     0.5641391 -0.06577197 168 0.00038 ***
##  8:     22.5      0.4976190     0.5656705 -0.05806955 168    0.003 **
##  9:     25.5      0.5392857     0.5874740 -0.03434793 168      0.06 .
## 10:     28.5      0.5017857     0.5711020 -0.06820755 168   0.0022 **
##     time  error.diff shapes
##  1:  1.5 -0.13598057     24
##  2:  4.5 -0.08536726     24
##  3:  7.5 -0.06382281     24
##  4: 10.5 -0.06568890     24
##  5: 13.5 -0.08101223     24
##  6: 16.5 -0.10807690     24
##  7: 19.5 -0.06577197     24
##  8: 22.5 -0.05806955     24
##  9: 25.5 -0.03434793     16
## 10: 28.5 -0.06820755     24

##     time.bin subj.diff.mean obj.diff.mean   error.diff   n        pval
##  1:      1.5      0.4355769     0.5969130 -0.167868594 104 3.2e-06 ***
##  2:      4.5      0.5089744     0.6297636 -0.133783305 156 3.6e-06 ***
##  3:      7.5      0.5102564     0.5544687 -0.055654906 156     0.036 *
##  4:     10.5      0.5224359     0.5229882 -0.002890341 156     0.89 :(
##  5:     13.5      0.5173077     0.5312208 -0.020469231 156     0.44 :(
##  6:     16.5      0.5102564     0.5008164  0.003037161 156     0.91 :(
##  7:     19.5      0.4576923     0.4456698  0.001732469 156     0.95 :(
##  8:     22.5      0.4211538     0.4198655 -0.005262489 156     0.84 :(
##  9:     25.5      0.4576923     0.3963862  0.067707055 156     0.015 *
## 10:     28.5      0.4435897     0.3637653  0.061919707 156     0.014 *
##     time   error.diff shapes
##  1:  1.5 -0.167868594     24
##  2:  4.5 -0.133783305     24
##  3:  7.5 -0.055654906     24
##  4: 10.5 -0.002890341     16
##  5: 13.5 -0.020469231     16
##  6: 16.5  0.003037161     16
##  7: 19.5  0.001732469     16
##  8: 22.5 -0.005262489     16
##  9: 25.5  0.067707055     24
## 10: 28.5  0.061919707     24

For all taks, per group

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTAll[niveau.group == "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.74912  -0.17163  -0.06171   0.23089   0.56845  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.26742    0.03231   8.276 5.24e-16 ***
## timeNorm     0.09327    0.03088   3.020  0.00261 ** 
## obj.diff    -0.60719    0.03298 -18.410  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.05918634)
## 
##     Null deviance: 69.725  on 811  degrees of freedom
## Residual deviance: 47.882  on 809  degrees of freedom
## AIC: 13.774
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTAll[niveau.group == "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.74792  -0.21648   0.00657   0.22536   0.74288  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.18943    0.01822  10.399   <2e-16 ***
## timeNorm     0.02871    0.02181   1.316    0.188    
## obj.diff    -0.43074    0.02041 -21.105   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06895638)
## 
##     Null deviance: 161.39  on 1884  degrees of freedom
## Residual deviance: 129.78  on 1882  degrees of freedom
## AIC: 313.38
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTAll[niveau.group == "good"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.7579  -0.1902  -0.0057   0.2034   0.7204  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.13887    0.01539   9.024   <2e-16 ***
## timeNorm     0.03309    0.01996   1.658   0.0974 .  
## obj.diff    -0.37629    0.01927 -19.528   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06202091)
## 
##     Null deviance: 152.70  on 2058  degrees of freedom
## Residual deviance: 127.51  on 2056  degrees of freedom
## AIC: 123.58
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5      0.5535714     0.7841094 -0.23943505 56 5.6e-07 ***
##  2:      4.5      0.5595238     0.7816709 -0.23846713 84 2.2e-08 ***
##  3:      7.5      0.6071429     0.7784863 -0.18907328 84 5.3e-07 ***
##  4:     10.5      0.6357143     0.7463297 -0.12116310 84   0.0011 **
##  5:     13.5      0.6357143     0.7693833 -0.16124892 84 2.7e-05 ***
##  6:     16.5      0.6047619     0.7336419 -0.14629401 84 0.00013 ***
##  7:     19.5      0.6273810     0.7080292 -0.09028143 84   0.0038 **
##  8:     22.5      0.6130952     0.7354310 -0.12161746 84 0.00078 ***
##  9:     25.5      0.6000000     0.6935483 -0.09262945 84     0.013 *
## 10:     28.5      0.6202381     0.6758574 -0.05105060 84     0.13 :(
##     time  error.diff shapes
##  1:  1.5 -0.23943505     24
##  2:  4.5 -0.23846713     24
##  3:  7.5 -0.18907328     24
##  4: 10.5 -0.12116310     24
##  5: 13.5 -0.16124892     24
##  6: 16.5 -0.14629401     24
##  7: 19.5 -0.09028143     24
##  8: 22.5 -0.12161746     24
##  9: 25.5 -0.09262945     24
## 10: 28.5 -0.05105060     16

##     time.bin subj.diff.mean obj.diff.mean   error.diff   n        pval
##  1:      1.5      0.5076923     0.6076861 -0.102575997 130 0.00044 ***
##  2:      4.5      0.5743590     0.6661471 -0.088370657 195   2e-05 ***
##  3:      7.5      0.5148718     0.5280778 -0.018922473 195     0.37 :(
##  4:     10.5      0.5482051     0.5747535 -0.024221699 195     0.29 :(
##  5:     13.5      0.5297436     0.5737278 -0.042820060 195     0.025 *
##  6:     16.5      0.5241026     0.5510275 -0.032238337 195     0.12 :(
##  7:     19.5      0.5015385     0.5677113 -0.068517865 195 0.00092 ***
##  8:     22.5      0.4928205     0.5156362 -0.028746807 195     0.18 :(
##  9:     25.5      0.5358974     0.5285704  0.004618592 195     0.83 :(
## 10:     28.5      0.5061538     0.5119649 -0.014162742 195     0.51 :(
##     time   error.diff shapes
##  1:  1.5 -0.102575997     24
##  2:  4.5 -0.088370657     24
##  3:  7.5 -0.018922473     16
##  4: 10.5 -0.024221699     16
##  5: 13.5 -0.042820060     24
##  6: 16.5 -0.032238337     16
##  7: 19.5 -0.068517865     24
##  8: 22.5 -0.028746807     16
##  9: 25.5  0.004618592     16
## 10: 28.5 -0.014162742     16

##     time.bin subj.diff.mean obj.diff.mean   error.diff   n      pval
##  1:      1.5      0.4281690     0.5141051 -0.080402452 142 0.0021 **
##  2:      4.5      0.4478873     0.4753359 -0.027143114 213   0.16 :(
##  3:      7.5      0.4309859     0.4608404 -0.028485150 213   0.14 :(
##  4:     10.5      0.4572770     0.4479476  0.013162796 213   0.49 :(
##  5:     13.5      0.4197183     0.4146644  0.010568298 213    0.6 :(
##  6:     16.5      0.4107981     0.4121246 -0.002777196 213    0.9 :(
##  7:     19.5      0.4056338     0.3916553  0.010544404 213   0.59 :(
##  8:     22.5      0.3849765     0.3778424  0.005899965 213   0.72 :(
##  9:     25.5      0.4117371     0.3752961  0.035408215 213   0.038 *
## 10:     28.5      0.3788732     0.3423094  0.026264248 213   0.17 :(
##     time   error.diff shapes
##  1:  1.5 -0.080402452     24
##  2:  4.5 -0.027143114     16
##  3:  7.5 -0.028485150     16
##  4: 10.5  0.013162796     16
##  5: 13.5  0.010568298     16
##  6: 16.5 -0.002777196     16
##  7: 19.5  0.010544404     16
##  8: 22.5  0.005899965     16
##  9: 25.5  0.035408215     24
## 10: 28.5  0.026264248     16

Per group, motor task

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTM[niveau.group == "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.65062  -0.16552  -0.07705   0.21881   0.38387  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.29158    0.07860   3.710  0.00026 ***
## timeNorm     0.04078    0.04734   0.861  0.38990    
## obj.diff    -0.58583    0.08967  -6.533 4.12e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.03968196)
## 
##     Null deviance: 10.9054  on 231  degrees of freedom
## Residual deviance:  9.0872  on 229  degrees of freedom
## AIC: -85.263
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5      0.6250000     0.8544830 -0.23067342 16   0.0013 **
##  2:      4.5      0.6375000     0.7995145 -0.16957820 24   0.0048 **
##  3:      7.5      0.6208333     0.7551085 -0.13379284 24     0.012 *
##  4:     10.5      0.6375000     0.7836615 -0.15718140 24   0.0079 **
##  5:     13.5      0.6250000     0.8240112 -0.20576489 24 6.4e-05 ***
##  6:     16.5      0.6375000     0.7818411 -0.15147782 24     0.027 *
##  7:     19.5      0.6541667     0.7263256 -0.07096924 24     0.13 :(
##  8:     22.5      0.6458333     0.7654436 -0.12523757 24     0.046 *
##  9:     25.5      0.6583333     0.7908307 -0.13301969 24   0.0072 **
## 10:     28.5      0.6166667     0.7394768 -0.11097038 24     0.039 *
##     time  error.diff shapes
##  1:  1.5 -0.23067342     24
##  2:  4.5 -0.16957820     24
##  3:  7.5 -0.13379284     24
##  4: 10.5 -0.15718140     24
##  5: 13.5 -0.20576489     24
##  6: 16.5 -0.15147782     24
##  7: 19.5 -0.07096924     16
##  8: 22.5 -0.12523757     24
##  9: 25.5 -0.13301969     24
## 10: 28.5 -0.11097038     24

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTM[niveau.group == "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.68934  -0.16575   0.00973   0.19104   0.67014  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.157587   0.040226   3.918 9.92e-05 ***
## timeNorm    -0.008747   0.037508  -0.233    0.816    
## obj.diff    -0.364236   0.054058  -6.738 3.61e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06744585)
## 
##     Null deviance: 45.961  on 637  degrees of freedom
## Residual deviance: 42.828  on 635  degrees of freedom
## AIC: 95.236
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff  n      pval
##  1:      1.5      0.5204545     0.6251419 -0.096882522 44   0.034 *
##  2:      4.5      0.5454545     0.6224524 -0.069888163 66   0.042 *
##  3:      7.5      0.5212121     0.5482212 -0.022392160 66   0.54 :(
##  4:     10.5      0.5257576     0.5744464 -0.036347555 66   0.35 :(
##  5:     13.5      0.5348485     0.5455378 -0.006192686 66   0.85 :(
##  6:     16.5      0.5272727     0.5560045 -0.033252815 66   0.35 :(
##  7:     19.5      0.4712121     0.5704673 -0.107605826 66 0.0013 **
##  8:     22.5      0.4439394     0.5060978 -0.066259279 66   0.063 .
##  9:     25.5      0.4787879     0.4999714 -0.024063555 66   0.54 :(
## 10:     28.5      0.4787879     0.5016324 -0.029290994 66   0.31 :(
##     time   error.diff shapes
##  1:  1.5 -0.096882522     24
##  2:  4.5 -0.069888163     24
##  3:  7.5 -0.022392160     16
##  4: 10.5 -0.036347555     16
##  5: 13.5 -0.006192686     16
##  6: 16.5 -0.033252815     16
##  7: 19.5 -0.107605826     24
##  8: 22.5 -0.066259279     16
##  9: 25.5 -0.024063555     16
## 10: 28.5 -0.029290994     16

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTM[niveau.group == "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.61934  -0.16018   0.01038   0.17385   0.53652  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.11181    0.02651   4.217 2.78e-05 ***
## timeNorm     0.02800    0.02850   0.983    0.326    
## obj.diff    -0.19693    0.04083  -4.823 1.71e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.04519554)
## 
##     Null deviance: 35.197  on 753  degrees of freedom
## Residual deviance: 33.942  on 751  degrees of freedom
## AIC: -190.2
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff  n      pval
##  1:      1.5      0.5192308     0.5021701  0.020553607 52   0.56 :(
##  2:      4.5      0.4820513     0.4581003  0.028602593 78   0.28 :(
##  3:      7.5      0.4602564     0.4705078 -0.007131425 78   0.76 :(
##  4:     10.5      0.5089744     0.4361551  0.085310624 78 0.0021 **
##  5:     13.5      0.4474359     0.3993679  0.055711847 78   0.043 *
##  6:     16.5      0.4846154     0.4316421  0.056312672 78   0.036 *
##  7:     19.5      0.4717949     0.4386951  0.030866623 78   0.22 :(
##  8:     22.5      0.4551282     0.3910376  0.068335238 78   0.013 *
##  9:     25.5      0.4256410     0.3686849  0.059334781 78   0.014 *
## 10:     28.5      0.4051282     0.3329405  0.069444110 78 0.0055 **
##     time   error.diff shapes
##  1:  1.5  0.020553607     16
##  2:  4.5  0.028602593     16
##  3:  7.5 -0.007131425     16
##  4: 10.5  0.085310624     24
##  5: 13.5  0.055711847     24
##  6: 16.5  0.056312672     24
##  7: 19.5  0.030866623     16
##  8: 22.5  0.068335238     24
##  9: 25.5  0.059334781     24
## 10: 28.5  0.069444110     24

Per group, sensory task

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTS[niveau.group == "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.73821  -0.20661  -0.03259   0.20583   0.62369  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.22002    0.04425   4.972 1.14e-06 ***
## timeNorm     0.03942    0.05278   0.747    0.456    
## obj.diff    -0.51835    0.04440 -11.675  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06277719)
## 
##     Null deviance: 26.645  on 289  degrees of freedom
## Residual deviance: 18.017  on 287  degrees of freedom
## AIC: 25.201
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n      pval
##  1:      1.5      0.5200000     0.6390463 -0.13867440 20   0.11 :(
##  2:      4.5      0.5233333     0.6686706 -0.14681000 30   0.023 *
##  3:      7.5      0.5600000     0.7179520 -0.16882939 30 0.0047 **
##  4:     10.5      0.6166667     0.7022945 -0.09167106 30   0.13 :(
##  5:     13.5      0.6300000     0.7355270 -0.09696383 30   0.047 *
##  6:     16.5      0.5033333     0.6316433 -0.17116360 30   0.026 *
##  7:     19.5      0.5666667     0.6735104 -0.14469214 30   0.061 .
##  8:     22.5      0.6766667     0.7285240 -0.04571003 30   0.52 :(
##  9:     25.5      0.5200000     0.6387517 -0.10658266 30    0.07 .
## 10:     28.5      0.5400000     0.6238117 -0.06667086 30   0.26 :(
##     time  error.diff shapes
##  1:  1.5 -0.13867440     16
##  2:  4.5 -0.14681000     24
##  3:  7.5 -0.16882939     24
##  4: 10.5 -0.09167106     16
##  5: 13.5 -0.09696383     24
##  6: 16.5 -0.17116360     24
##  7: 19.5 -0.14469214     16
##  8: 22.5 -0.04571003     16
##  9: 25.5 -0.10658266     16
## 10: 28.5 -0.06667086     16

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTS[niveau.group == "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.72208  -0.20591   0.01923   0.20060   0.76828  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.11070    0.02506   4.417 1.14e-05 ***
## timeNorm     0.03910    0.03334   1.173    0.241    
## obj.diff    -0.31529    0.02603 -12.114  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06768712)
## 
##     Null deviance: 62.835  on 782  degrees of freedom
## Residual deviance: 52.796  on 780  degrees of freedom
## AIC: 118.54
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff  n      pval
##  1:      1.5      0.5185185     0.5856813 -0.077995802 54   0.068 .
##  2:      4.5      0.5839506     0.6540986 -0.051214048 81 0.0094 **
##  3:      7.5      0.4753086     0.4885319 -0.021685483 81    0.5 :(
##  4:     10.5      0.5246914     0.5978029 -0.068227022 81   0.038 *
##  5:     13.5      0.4975309     0.5802643 -0.074552535 81 0.0055 **
##  6:     16.5      0.4765432     0.5255582 -0.048653420 81   0.12 :(
##  7:     19.5      0.5222222     0.5760814 -0.029460170 81   0.19 :(
##  8:     22.5      0.4962963     0.5370262 -0.035337710 81   0.12 :(
##  9:     25.5      0.5827160     0.5877937 -0.003735285 81   0.87 :(
## 10:     28.5      0.5555556     0.5957763 -0.046408481 81   0.12 :(
##     time   error.diff shapes
##  1:  1.5 -0.077995802     16
##  2:  4.5 -0.051214048     24
##  3:  7.5 -0.021685483     16
##  4: 10.5 -0.068227022     24
##  5: 13.5 -0.074552535     24
##  6: 16.5 -0.048653420     16
##  7: 19.5 -0.029460170     16
##  8: 22.5 -0.035337710     16
##  9: 25.5 -0.003735285     16
## 10: 28.5 -0.046408481     16

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTS[niveau.group == "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.68106  -0.15992  -0.08839   0.24816   0.75224  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.07430    0.02812   2.642  0.00847 ** 
## timeNorm     0.03609    0.03932   0.918  0.35921    
## obj.diff    -0.38936    0.02997 -12.991  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06627138)
## 
##     Null deviance: 47.552  on 550  degrees of freedom
## Residual deviance: 36.317  on 548  degrees of freedom
## AIC: 73.25
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5      0.3736842     0.5824937 -0.22105237 38 0.00032 ***
##  2:      4.5      0.4052632     0.5178656 -0.09997599 57   0.0035 **
##  3:      7.5      0.4052632     0.4897451 -0.07737910 57     0.037 *
##  4:     10.5      0.4473684     0.4975966 -0.05160426 57     0.089 .
##  5:     13.5      0.3684211     0.4554127 -0.07786792 57     0.016 *
##  6:     16.5      0.3385965     0.4926908 -0.16375666 57 9.9e-05 ***
##  7:     19.5      0.3964912     0.4896047 -0.07782690 57    0.006 **
##  8:     22.5      0.4052632     0.5206631 -0.10748829 57   0.0074 **
##  9:     25.5      0.4877193     0.5600315 -0.04713849 57      0.1 :(
## 10:     28.5      0.4052632     0.5082965 -0.10381377 57   0.0058 **
##     time  error.diff shapes
##  1:  1.5 -0.22105237     24
##  2:  4.5 -0.09997599     24
##  3:  7.5 -0.07737910     24
##  4: 10.5 -0.05160426     16
##  5: 13.5 -0.07786792     24
##  6: 16.5 -0.16375666     24
##  7: 19.5 -0.07782690     24
##  8: 22.5 -0.10748829     24
##  9: 25.5 -0.04713849     16
## 10: 28.5 -0.10381377     24

Per group, logical task

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTL[niveau.group == "bad"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.7214  -0.1464  -0.0928   0.2761   0.4828  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.39347    0.06883   5.716 2.73e-08 ***
## timeNorm     0.14415    0.05746   2.509   0.0127 *  
## obj.diff    -0.79904    0.06673 -11.974  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.0679132)
## 
##     Null deviance: 31.964  on 289  degrees of freedom
## Residual deviance: 19.491  on 287  degrees of freedom
## AIC: 48.007
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5      0.5300000     0.8728737 -0.35721006 20 1.3e-05 ***
##  2:      4.5      0.5333333     0.8803963 -0.41988468 30 1.6e-07 ***
##  3:      7.5      0.6433333     0.8577229 -0.24690480 30   0.0011 **
##  4:     10.5      0.6533333     0.7604994 -0.11216598 30      0.1 :(
##  5:     13.5      0.6500000     0.7595374 -0.15422943 30     0.084 .
##  6:     16.5      0.6800000     0.7970813 -0.11943071 30     0.067 .
##  7:     19.5      0.6666667     0.7279108 -0.05082550 30     0.23 :(
##  8:     22.5      0.5233333     0.7183280 -0.19772044 30   0.0022 **
##  9:     25.5      0.6333333     0.6705190 -0.02532747 30     0.84 :(
## 10:     28.5      0.7033333     0.6770076  0.01918051 30     0.75 :(
##     time  error.diff shapes
##  1:  1.5 -0.35721006     24
##  2:  4.5 -0.41988468     24
##  3:  7.5 -0.24690480     24
##  4: 10.5 -0.11216598     16
##  5: 13.5 -0.15422943     16
##  6: 16.5 -0.11943071     16
##  7: 19.5 -0.05082550     16
##  8: 22.5 -0.19772044     24
##  9: 25.5 -0.02532747     16
## 10: 28.5  0.01918051     16
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 2 rows containing missing values (geom_errorbar).

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTL[niveau.group == "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.66540  -0.13556  -0.01914   0.14637   0.56211  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.41736    0.03657  11.412   <2e-16 ***
## timeNorm    -0.01170    0.04229  -0.277    0.782    
## obj.diff    -0.74466    0.03851 -19.335   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.060379)
## 
##     Null deviance: 51.798  on 463  degrees of freedom
## Residual deviance: 27.835  on 461  degrees of freedom
## AIC: 19.264
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff  n      pval
##  1:      1.5      0.4718750     0.6208173 -0.156297520 32   0.023 *
##  2:      4.5      0.5979167     0.7465592 -0.162782730 48 0.0013 **
##  3:      7.5      0.5729167     0.5671145 -0.014310820 48    0.8 :(
##  4:     10.5      0.6187500     0.5362800  0.076353265 48   0.18 :(
##  5:     13.5      0.5770833     0.6014588 -0.033945075 48    0.5 :(
##  6:     16.5      0.6000000     0.5871636 -0.003982046 48   0.95 :(
##  7:     19.5      0.5083333     0.5497972 -0.046822122 48   0.31 :(
##  8:     22.5      0.5541667     0.4926560  0.064059938 48   0.24 :(
##  9:     25.5      0.5354167     0.4679547  0.073088155 48   0.15 :(
## 10:     28.5      0.4604167     0.3847404  0.083232356 48   0.17 :(
##     time   error.diff shapes
##  1:  1.5 -0.156297520     24
##  2:  4.5 -0.162782730     24
##  3:  7.5 -0.014310820     16
##  4: 10.5  0.076353265     16
##  5: 13.5 -0.033945075     16
##  6: 16.5 -0.003982046     16
##  7: 19.5 -0.046822122     16
##  8: 22.5  0.064059938     16
##  9: 25.5  0.073088155     16
## 10: 28.5  0.083232356     16

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTL[niveau.group == "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.65768  -0.19763  -0.04035   0.21009   0.72371  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.12696    0.02761   4.598    5e-06 ***
## timeNorm     0.06463    0.03613   1.789    0.074 .  
## obj.diff    -0.37517    0.03533 -10.619   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06922719)
## 
##     Null deviance: 61.766  on 753  degrees of freedom
## Residual deviance: 51.990  on 751  degrees of freedom
## AIC: 131.3
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff  n    pval
##  1:      1.5      0.3769231     0.4760639 -0.102437221 52 0.042 *
##  2:      4.5      0.4448718     0.4614922 -0.027765478 78 0.44 :(
##  3:      7.5      0.4205128     0.4300504 -0.017443021 78 0.62 :(
##  4:     10.5      0.4128205     0.4234581 -0.008671569 78 0.81 :(
##  5:     13.5      0.4294872     0.4001833  0.034151496 78 0.44 :(
##  6:     16.5      0.3897436     0.3337317  0.052219011 78 0.13 :(
##  7:     19.5      0.3461538     0.2730373  0.067485507 78 0.073 .
##  8:     22.5      0.3000000     0.2602781  0.014460765 78 0.59 :(
##  9:     25.5      0.3423077     0.2469083  0.092629756 78 0.011 *
## 10:     28.5      0.3333333     0.2303798  0.075968753 78 0.048 *
##     time   error.diff shapes
##  1:  1.5 -0.102437221     24
##  2:  4.5 -0.027765478     16
##  3:  7.5 -0.017443021     16
##  4: 10.5 -0.008671569     16
##  5: 13.5  0.034151496     16
##  6: 16.5  0.052219011     16
##  7: 19.5  0.067485507     16
##  8: 22.5  0.014460765     16
##  9: 25.5  0.092629756     24
## 10: 28.5  0.075968753     24

{r plot.subjective.objective.difficulty.confidence.scale, echo=FALSE} # #-------------------------------------------------------------------------------------- # # SHOWING SUBJECTIVE VS OBJECTIVE DIFFICULTY (CONFIDENCE SCALE APPROACH) # #-------------------------------------------------------------------------------------- # # plot.subjective.difficulty <- function(DT,selGroup,title){ # # print(selGroup) # # # Lien entre mise normalisée et difficultée estimée (hard / easy effect) # obj.diff.quants = seq(0,1,1/16)#quantile(DT$obj.diff, probs=(seq(0,1,0.05))) # nb.bins = length(obj.diff.quants)-1 # subj.diff.med = numeric(nb.bins) # obj.diff.bin = numeric(nb.bins) # obj.diff.bin.cur = 0; # test.pvals = numeric(nb.bins) # conf.min = numeric(nb.bins) # conf.max = numeric(nb.bins) # nb.vals = numeric(nb.bins) # shapes = numeric(nb.bins) # delta.obj.subj = numeric(nb.bins) # hist(DT$obj.diff) # for(i in 1:nb.bins){ # #obj.diff.bin.cur = round(i/10,1) # #subj.diff = DT[round(obj.diff,1)==obj.diff.bin.cur]$subj.diff.mise # obj.diff.bin.cur = (obj.diff.quants[i] + obj.diff.quants[i+1])/2.0 # #subj.diff = DT[obj.diff > obj.diff.quants[i] & obj.diff<=obj.diff.quants[i+1]]$subj.diff.mise # DTLoc = DT[obj.diff > obj.diff.quants[i] & obj.diff<=obj.diff.quants[i+1]] # if(selGroup != "all") # DTLoc = DTLoc[niveau.group==selGroup] # DTLoc = DTLoc[,.(confiance.mean=mean(subj.diff.confiance)),by=IDjoueur] # subj.diff = DTLoc$confiance.mean # obj.diff.bin[i] = obj.diff.bin.cur # subj.diff.med[i] = NA # test.pvals[i] = NA # conf.min[i] = NA # conf.max[i] = NA # delta.obj.subj[i] = NA # shapes[i] = 16 # nb.vals[i] = length(subj.diff) # if(nb.vals[i] > 1){ # try.res = try(test.res <- wilcox.test(subj.diff,mu = obj.diff.bin.cur,conf.int=T)) # if (class(try.res) != "try-error"){ # #print(test.res) # #hist(subj.diff) # test.pvals[i] = format.pval.stars(test.res$p.value) # if(test.res$p.value < 0.05) # shapes[i] = 24 # #subj.diff.med[i] = mean(subj.diff) # subj.diff.med[i] = test.res$estimate # conf.min[i] = test.res$conf.int[1] # conf.max[i] = test.res$conf.int[2] # delta.obj.subj[i] = signif(subj.diff.med[i] - obj.diff.bin.cur,digit=2) # } # } # } # # #print table of pvalues # print(data.table(obj.diff.bin=obj.diff.bin,delta.obj.subj=delta.obj.subj,n=nb.vals,pval=test.pvals)) # # #summary # print("mean and sd of nb players per bin") # DTNbVals = data.table(nb = nb.vals, pval=test.pvals) # print(DTNbVals[!is.na(pval)]) # print(signif(mean(DTNbVals[!is.na(pval)]$nb),digits=3)) # print(signif(sd(DTNbVals[!is.na(pval)]$nb),digits=3)) # # #kernel smooth # subj.diff.smooth <- ksmooth(x=DT$obj.diff,y=DT$subj.diff.confiance,bandwidth = 0.2) # DTSmooth = data.table(x=subj.diff.smooth$x,y=subj.diff.smooth$y) # # DTPlot = data.table(obj.diff=obj.diff.bin,subj.diff=subj.diff.med, shapes=shapes) # # p = ggplot() + ggtitle(title) + # # geom_line(aes(x=DTPouet$x,y=DTPouet$y))+ # geom_point(aes(x=DTPlot$obj.diff,y=DTPlot$subj.diff),alpha = 1, size = 3, shape=DTPlot$shapes) + # xlim(0,1)+ # ylim(0,1)+ # geom_errorbar(aes(x=DTPlot$obj.diff, ymin=conf.min, ymax=conf.max), width=.01,color="red") + # geom_abline(intercept = 0, slope = 1, color="blue") + # xlab("Objective Difficulty") + ylab("Subjective Difficulty") + theme(text = element_text(size=15)) # # print(p) # } #

All tasks

{r plot.subjective.difficulty.all.confidence.scale, echo=FALSE} # plot.subjective.difficulty(DTAll,"all", "All tasks, all groups") # plot.subjective.difficulty(DTAll,"good", "All tasks, good") # plot.subjective.difficulty(DTAll,"medium", "All tasks, medium") # plot.subjective.difficulty(DTAll,"bad", "All tasks, bad") #

Motor task

{r plot.subjective.difficulty.motor.confidence.scale, echo=FALSE} # plot.subjective.difficulty(DTM,"all", "Motor, all") # plot.subjective.difficulty(DTM,"good", "Motor, good") # plot.subjective.difficulty(DTM,"medium", "Motor, medium") # plot.subjective.difficulty(DTM,"bad", "Motor, bad") #

Sensory task

{r plot.subjective.difficulty.sensory.confidence.scale, echo=FALSE} # plot.subjective.difficulty(DTS,"all","Sensory, all") # plot.subjective.difficulty(DTS,"good","Sensory, good") # plot.subjective.difficulty(DTS,"medium","Sensory, medium") # plot.subjective.difficulty(DTS,"bad","Sensory, bad") #

Logical task

{r plot.subjective.difficulty.logical.confidence.scale, echo=FALSE} # plot.subjective.difficulty(DTL,"all","Logical, all") # plot.subjective.difficulty(DTL,"good","Logical, good") # plot.subjective.difficulty(DTL,"medium","Logical, medium") # plot.subjective.difficulty(DTL,"bad","Logical, bad") #